An Architecture for Cooperation among Autonomous Agents - CiteSeerX

An Architecture for Cooperation among Autonomous Agents

A thesis submitted in fulfilment of the requirements for the award of the degree of

Doctor of Philosophy

from

THE UNIVERSITY OF WOLLONGONG

by

David Jung B.Sc Hons. (Faculty of Computer and Mathematical Sciences), University of Adelaide. Grad. Cert. Software Engineering, University of South Australia.

©1998 by David Jung. All rights reserved.

Intelligent Robotics Laboratory Department of Computer Science

I hereby declare that I am the sole author of this thesis. I also declare that the material presented within is my own work, except where duly acknowledged, and that I am not aware of any similar work either prior to this thesis or currently being pursued.

David Jung

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Abstract This thesis develops the Architecture for Behaviour Based Agents (ABBA) – an architecture suitable for supporting the distributed planning of cooperative behaviour in multi-robot systems. ABBA was used to implement a concrete cooperative cleaning task using two mobile robots, both to drive the design requirements and as a demonstration of its efficacy. The cleaning task solution requires reactive and deliberative behaviour, purposive navigation by learning unknown environments, cooperation and communication. Learning to navigate purposively in an unknown environment obviously requires a map. In adherence with the behaviour-based philosophy, the navigation mechanism developed uses no explicit representation. The map representation arises as a natural consequence of the action selection dynamics, correlation learning and the key notion of feature detectors for ‘locations’. The robot learns the spatial and topological adjacency of locations through behaving. The implementation of this mechanism demonstrates, for the first time, that situated, embodied agents without an explicitly specified map representation are capable of purposive navigation including spatial and topological path planning using a homogeneous action selection substrate. A mechanism for truly distributed planning of cooperative behaviour in behaviour-based agents is also developed. Cooperative planning is considered an extension of the actionselection problem facing individual agents. The rationale being, that if the interaction between behaviour elements within an agent can accomplish action planning, then the interaction between behaviour elements among agents can accomplish cooperative action planning. The only difference is the possible methods of interaction. Where the action planning of one agent depends on states internal to another, communication is needed. By analogy to interaction schemes observed in biological systems, the implementation of the cleaning task is layered. Each layer builds the sophistication of cooperation and communication by relying on the layers below it. Results show that, in addition to being a design aid for managing complexity, layering greatly increases the robustness of the system. The top layer of the system employs communication involving a symbol – for ‘locations’. As communicated signals carry no intrinsic meaning, a mechanism for pre-arranging for a shared grounding for the symbols, that utilises vision, is presented. The location labelling procedure demonstrates grounded symbolic communication between behaviour-based agents – albeit in a narrow context involving a single symbol type. Results show that symbolic communication of location information greatly enhances the performance of cleaning. The implementation of a concrete task required construction of two Yamabico mobile robots and equipping them with sensors and actuators suitable for cleaning. Bump sensors, a vacuum cleaner, and a unique proportional whisker sensor for wall following were developed and fitted. Radio networking software (RADNET) was developed enabling inter-robot and robot-host communication to support the experiments. Visual mechanisms for obstacle avoidance, robot identification and tracking and litter pile location were also developed. The implementation of which use real-time hardware-based template matching in a novel way.

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Acknowledgments Where do I begin? At the start of this PhD, I spent three very enjoyable months living and working in Japan at Professor Yuta’s laboratory at the University of Tsukuba. I would like to thank all the members of the Intelligent Robotics Laboratory for their hospitality and for sharing the Japanese culture with us Aussies (Ben Stanley & myself). I’d also like to thank everyone at the Wollongong Intelligent Robotics laboratory where I started by PhD. The majority of my time was spent working at the newly formed Robotics Systems Laboratory (RSL) at the Australian National University (ANU) in Canberra, headed by my supervisor Alex Zelinsky, who imported Gordon Cheng and myself from Wollongong. I’d like to thank the department of Systems Engineering for accepting me as a visitor for this time. It has been a great place to work – both because of the facilities, but mostly due to the people in the department. Alex has done a wonderful job of nurturing a cooperative and friendly environment in the robotics group with people from many backgrounds. I have been lucky enough to make friends with many of the overseas visitors that regularly work with us for short periods. Thanks to everyone who has supported our work – including Marita Rendina, Jenni Watkins and especially James Ashton, who is always helpful when we’re running around with deadlines and problems. Of course, none of this would have been so enjoyable without my ‘old’ lab buddies Gordon Cheng and Jochen Heinzmann – thanks guys. Thanks also to my newer lab buddies Andrew ‘zoz’ Brooks, Jacob Lildballe, Giovanni Bianco, Yoshio Matsumoto, Llew Mason, and Louis Shue – without which Dinner, the Lab, and Quake would not have been as much fun. More thanks go to Chris ‘homebrew’ Gaskett, Rochelle O’Hagan, Samer Abdallah, Ben Stanley and Phillip McKerrow. Thanks Alex for being my supervisor, giving me the opportunity, and giving me the freedom and help I needed. Hard to believe it has been over three years already. My appreciation to both Alex and also David Wettergreen for reading drafts of my thesis and giving advice. I’d like to thank Gordon’s wife Katrina for her many good home-cooked meals and hospitality – and also their kids Jamie, Maddie, Andrew and Nick – for being entertaining and supplying my new-age office décor. Very special thanks to Meg Vost, for putting up with me while my PhD was my life, and to my parents Pauline and Bob (also known as Mum&Dad), for supporting me in my efforts.

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Publication List Jung, David, Cheng, Gordon and Zelinsky, Alexander, “Robot Cleaning: An Application of Distributed Planning and Real-time Vision”, Field and Service Robotics, Springer, Alexander Zelinsky (ed.), pp187194, 1998. Jung, David and Zelinsky, Alexander, “An Architecture for Distributed Cooperative-Planning in a Behaviour-based Multi-robot System”, Journal of Robotics & Autonomous Systems (RA&S) 26, special issue on Field & Service Robotics, pp149-174, 1999. Jung, David, Heinzmann, Jochen and Zelinsky, Alexander, “Range and Pose Estimation for Visual Servoing on a Mobile Robotic Target”, Proc. IEEE International Conference on Robotics and Automation (ICRA), vol. 2 pp1226-1231, Leuven, Belgium, May 1998. Jung, David and Zelinsky, Alexander, “Whisker-Based Mobile Robot Navigation”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol 2. pp497-504, November 1996. Jung, David, Cheng, Gordon and Zelinsky, Alexander, “An Experiment in Realising Cooperation between Autonomous Mobile Robots”, Fifth International Symposium on Experimental Robotics (ISER), pp513-524, Barcelona, Catelonia, June 1997. Jung, David and Zelinsky, Alexander, “Cleaning: An Experiment in Autonomous Robot Cooperation”, Proceedings of the World Automation Congress (WAC) - International Symposium on Robotics and Manufacturing (ISRAM), vol 3. pp351-356, Montipellier, France, March, 1996. Jung, David, Cheng, Gordon and Zelinsky, Alexander, “Robot Cleaning: An Application of Distributed Planning and Real-time Vision”, International conference on Field and Service Robotics (FSR), pp513524, Canberra, Australia, 1997. Jung, David, Stanley, Ben, Zelinsky, Alex, McKerrow, Phillip, Ohya, Akihisa, Tsubouchi, Takashi and Yuta, Shin’ichi, “Collaborative Research in Intelligent Autonomous Systems between the Universities of Wollongong and Tsukuba”, Proceedings of the Australian Robot Association (ARA) conference, Melbourne, July, pp222-229, 1995

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Table of Contents CHAPTER 1 INTRODUCTION ........................................................................1 1.1 Overview................................................................................................................... ................... 1 1.2 Outline .................................................................................................................... ..................... 4

CHAPTER 2 PHILOSOPHY ............................................................................6 2.1 The seeds of AI from 38000BC ............................................................................................... ... 7 2.1.1 Primates and Panpsychism ................................................................................................. . 8 2.1.2 Eastern Mysticism ............................................................................................................... 8 2.1.3 Western Culture and the Cartesian Divide .......................................................................... 9 2.1.4 Cognitive Psychology.......................................................................................................... 9 2.1.5 Classical Artificial Intelligence ......................................................................................... 10 2.1.6 Robotics ............................................................................................................................ 10 2.1.7 Summary ........................................................................................................................... 12 2.2 Symbols ...................................................................................................................................... 13 2.2.1 Representation................................................................................................................... 13 2.2.2 Language ........................................................................................................................... 15 2.3 The Pitfalls of Reductionism .................................................................................................... 18 2.3.1 Anthropocentric Categorisation ........................................................................................ 18 2.3.2 Behaviour-Based Robotics................................................................................................ 19 2.3.3 Learning ............................................................................................................................ 21 2.3.4 Implications for Robotics .................................................................................................. 21 2.4 Summary.................................................................................................................................... 24

CHAPTER 3 COOPERATION .......................................................................25 3.1 How can cooperation benefit Robotics?.................................................................................. 26 3.2 What is Cooperation? ............................................................................................................... 29 3.2.1 In the Literature................................................................................................................. 29 3.2.2 Why Cooperate?................................................................................................................ 31 3.3 Cooperation in Biological Systems .......................................................................................... 34 3.3.1 Cellular Cooperation ......................................................................................................... 35 3.3.2 Social Insect Societies....................................................................................................... 36 3.3.3 Cooperative Behaviour in Animals ................................................................................... 36 3.3.4 Primate Cooperation.......................................................................................................... 37 3.3.5 Summary ........................................................................................................................... 38 3.4 Cooperation in Robotics ........................................................................................................... 39 3.4.1 General Control Architectures........................................................................................... 39 3.4.2 Cooperative Systems ......................................................................................................... 43 3.4.3 Summary ........................................................................................................................... 45 3.5 Summary.................................................................................................................................... 46

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CHAPTER 4 A COOPERATIVE ARCHITECTURE .......................................48 4.1 Cooperative Cleaning ............................................................................................................... 49 4.2 Design of the Architecture........................................................................................................ 50 4.2.1 Action Selection ................................................................................................................ 50 4.2.2 Navigation......................................................................................................................... 55 4.2.3 Cooperation and Communication...................................................................................... 58 4.3 A Layered Solution ................................................................................................................... 64 4.3.1 Emergent Cooperation....................................................................................................... 65 4.3.2 Cooperation by Observation.............................................................................................. 66 4.3.3 Cooperation with Communication..................................................................................... 67 4.3.4 Cooperation by Planning................................................................................................... 68 4.3.5 Summary ........................................................................................................................... 69 4.4 ABBA Behaviour Networks ..................................................................................................... 69 4.4.1 Components and Interconnections .................................................................................... 69 4.4.2 How it works ..................................................................................................................... 71 4.4.3 The Spreading of Activation ............................................................................................. 74 4.4.4 The Algorithm................................................................................................................... 76 4.5 The Implementation.................................................................................................................. 77 4.6 Summary.................................................................................................................................... 79

CHAPTER 5 BEHAVIOUR.............................................................................80 5.1 Hardware................................................................................................................................... 80 5.1.1 ‘Yamabico’ Autonomous Mobile Robots.......................................................................... 81 5.1.2 Whiskers............................................................................................................................ 84 5.1.3 The Fujitsu Vision System ................................................................................................ 86 5.1.4 Communications................................................................................................................ 88 5.2 Behaviour Components ............................................................................................................ 90 5.2.1 Whisker Behaviours .......................................................................................................... 90 5.2.2 Visual Behaviours ............................................................................................................. 94 5.2.3 Miscellaneous Behaviours............................................................................................... 106 5.2.4 Summary ......................................................................................................................... 107 5.3 High-level Behaviour in ABBA.............................................................................................. 108 5.3.1 Navigation....................................................................................................................... 108 5.3.2 Deictic Reference ............................................................................................................ 121 5.3.3 Cooperation and Communication.................................................................................... 121 5.4 Summary.................................................................................................................................. 125

CHAPTER 6 COOPERATIVE EXPERIMENTS ...........................................126 6.1 Experiments................................................................................................................ ............. 126 6.1.1 Emergent Cooperation..................................................................................................... 126 6.1.2 Cooperation by Observation............................................................................................ 132 6.1.3 Cooperation with Communication................................................................................... 133 6.1.4 Cooperation by Planning................................................................................................. 137 6.2 Results...................................................................................................................................... 140 6.2.1 Performance .................................................................................................................... 140 6.2.2 Robustness ...................................................................................................................... 143

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6.2.3 Summary .................................................................................................................. ....... 144 6.3 Discussion ................................................................................................................................ 145 6.3.1 Would ‘sense-plan-act’ have worked? ............................................................................ 145 6.3.2 Comments on ABBA....................................................................................................... 146 6.3.3 Eliminating implementation difficulties .......................................................................... 148

CHAPTER 7 CONCLUSIONS .....................................................................150 7.1 Conclusions.............................................................................................................................. 150 7.2 Future Directions .................................................................................................................... 152 7.3 Summary.................................................................................................................................. 154

BIBLIOGRAPHY..........................................................................................155 GLOSSARY .................................................................................................166 APPENDIX A CDROM.................................................................................168

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List of Figures FIGURE 1 - RELATIONSHIPS BETWEEN SOME HUMAN ENDEAVOURS ..............................................................7 FIGURE 2 - THE ’SENSE-PLAN-ACT’ PARADIGM ............................................................................................11 FIGURE 3 - SUBSUMPTION ARCHITECTURE ..................................................................................................11 FIGURE 4 - CLASSICAL (TOP) VERSES BEHAVIOUR-BASED (BOTTOM) DECOMPOSITION ...............................12 FIGURE 5 - CONFLICT BETWEEN COLOUR AND WORDS ...............................................................................16 FIGURE 6 - RELIABILITY VS COMPLEXITY ....................................................................................................26 FIGURE 7 - COMPARISON OF RELIABILITY ....................................................................................................28 FIGURE 8 - SUBSUMPTION ARCHITECTURE ..................................................................................................41 FIGURE 9 - FLO SWEEPING AND JOH VACUUMING A LITTER PILE ..................................................................49 FIGURE 10 - MAES’ SAND BOARD EXAMPLE NETWORK ..............................................................................51 FIGURE 11 - TEMPORAL MAPPING OF FEATURE CONDITIONS ........................................................................54 FIGURE 12 - (A) GEOMETRIC VS (B) TOPOLOGICAL PATH PLANNING ..........................................................55 FIGURE 13 - LOCATION FDS SPAN THE SPATIAL ENVIRONMENT...................................................................57 FIGURE 14 - SEQUENCE OF ACTIONS AND THEIR EFFECTS ............................................................................59 FIGURE 15 - ABBA NETWORK FOR ACTION SEQUENCE ...............................................................................59 FIGURE 16 - TWO ROBOT ACTION SEQUENCE NETWORKS ............................................................................60 FIGURE 17 - ACTION SEQUENCE NETWORKS FOR HETEROGENEOUS ROBOTS ...............................................61 FIGURE 18 - ALTERNATIVE PLANS DEPENDANT ON CONDITION X.................................................................62 FIGURE 19 - ABBA NETWORKS FOR RENDEZVOUS DIALOGUE ....................................................................63 FIGURE 20 - CLEANING BY EMERGENT COOPERATION .................................................................................65 FIGURE 21 - COOPERATION BY OBSERVATION .............................................................................................66 FIGURE 22 - COOPERATION WITH COMMUNICATION ....................................................................................67 FIGURE 23 - COOPERATION BY PLANNING WITH SYMBOLIC COMMUNICATION.............................................68 FIGURE 24 - ABBA NETWORK COMPONENTS AND INTERCONNECTIONS .....................................................69 FIGURE 25 - SAND BOARD EXAMPLE IN ABBA ...........................................................................................72 FIGURE 26 - SAND BOARD EXAMPLE - ACTIVATION LINKS...........................................................................73 FIGURE 27 - ABBA IMPLEMENTATION ARCHITECTURE ...............................................................................77 FIGURE 28 - SCREEN SHOT OF ABBA GUI..................................................................................................78 FIGURE 29 - THE TWO YAMABICOS 'FLO' AND 'JOH' ....................................................................................81 FIGURE 30 - YAMABICO COMPUTER ARCHITECTURE ...................................................................................82 FIGURE 31 - YAMABICO TRANSPUTER LOCOMOTION MODULE (LEFT) AND ULTRASONIC SENSOR (RIGHT) ...82 FIGURE 32 - BUMPER STRIP FOR PROTECTION WHEN VACUUMING (LEFT) AND CONTROL BOARD (RIGHT)....83 FIGURE 33 - CUSTOM VACUUM CLEANER (LEFT) AND COMMERCIAL RADIO MODEM (RIGHT) ......................83 FIGURE 34 - WHISKER AND ULTRASONIC SENSOR PLACEMENT ON FLO ......................................................84 FIGURE 35 - A BINARY WHISKER .................................................................................................................85 FIGURE 36 - PROPORTIONAL WHISKER USING A ROTATIONAL VARIABLE RESISTOR ......................................86 FIGURE 37 - FLO'S WHISKERS (LEFT) AND THE CONTROL CIRCUIT (RIGHT)..................................................86 FIGURE 38 - HARDWARE CONFIGURATION...................................................................................................87 FIGURE 39 - RADNET PHYSICAL ARCHITECTURE ......................................................................................89 FIGURE 40 - ROBOT-WALL GEOMETRY ........................................................................................................92 FIGURE 41 - ROBOT AND WHISKER GEOMETRY............................................................................................93 FIGURE 42 - DISTANCE TO WALL: MODEL ONLY ESTIMATE VS. KALMAN FILTER ESTIMATE.........................94 FIGURE 43 - BEFORE AND AFTER NORMALISATION AND THRESHOLDING .....................................................95 FIGURE 44 - OBSTACLE AVOIDANCE USING FREE-SPACE SEGMENTATION ....................................................95 FIGURE 45 - FLO'S SIDE AS SEEN FROM JOH'S CCD CAMERA .......................................................................96 FIGURE 46 - PERSPECTIVE FORESHORTENING ..............................................................................................99 FIGURE 47 - MOMENT DURING CLEANING WHILE FLO IS IN VIEW ...............................................................102 FIGURE 48 - VISUAL SERVOING (WITH RANGE AND BEARING)....................................................................103 FIGURE 49 - LOSS AND RE-ACQUISITION OF TRACKING ..............................................................................104 FIGURE 50 - TRACKING SIZE INVARIANCE..................................................................................................104

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FIGURE 51 - ’INTEREST OPERATOR’............................................................................................................105 FIGURE 52 - A LITTER PILE (TOP VIEW) .....................................................................................................105 FIGURE 53 - NON-UNIFORM DISTRIBUTION OF FDS OVER THE FLOOR (PLAN VIEW)....................................109 FIGURE 54 - LOCATION FDS CONNECTED VIA ’FORWARD’ CM ..................................................................109 FIGURE 55 - CORRELATION BETWEEN FOLLOW CM AND CORNER LOCATION FDS ....................................110 FIGURE 56 - CORRELATION BETWEEN MULTIPLE FOLLOW CMS AND CORNER LOCATION FDS ..................111 FIGURE 57 - PLANNING AROUND AN OBSTACLE .........................................................................................112 FIGURE 58 - ACTIVATIONS WHEN PLANNING AROUND AN OBSTACLE (1) ...................................................113 FIGURE 59 - ACTIVATIONS WHEN PLANNING AROUND AN OBSTACLE (2) ...................................................114 FIGURE 60 - SOM AFTER 1000 ITERATIONS OF UNIFORM AND GAUSSIAN DISTRIBUTIONS .........................116 FIGURE 61 - SOM GENERATED BY FLO OVER PART OF OUR LAB ...............................................................118 FIGURE 62 - TEST TRAJECTORY (HAND DRAWN)........................................................................................119 FIGURE 63 - UNCORRECTED RECORDED ODOMETRIC TRAJECTORY ...........................................................120 FIGURE 64 - LANDMARK CORRECTED RECORDED ODOMETRIC TRAJECTORY .............................................120 FIGURE 65 - JOH’S LOCATION LABELLING NETWORK ................................................................................123 FIGURE 66 - JOH’S LITTER PILE LOCATING NETWORK ................................................................................124 FIGURE 67 - TYPICAL TRAJECTORIES OF FLO DURING EMERGENT COOPERATION .......................................127 FIGURE 68 - TYPICAL TRAJECTORY OF JOH DURING EMERGENT COOPERATION .........................................128 FIGURE 69 - FLO'S SWEEP AND DUMP NETWORK FOR EMERGENT COOPERATION ........................................129 FIGURE 70 - JOH WANDER AND VACUUM NETWORK FOR EMERGENT COOPERATION ..................................130 FIGURE 71 - FLO CM ACTIVATIONS WHILE PERIMITER SWEEPING ..............................................................131 FIGURE 72 - TYPICAL TRAJECTORIES DURING COOPERATION BY OBSERVATION ........................................132 FIGURE 73 - JOH NETWORK FOR COOPERATION BY OBSERVATION .............................................................133 FIGURE 74 - TYPICAL TRAJECTORIES DURING COOPERATION WITH COMMUNICATION................................133 FIGURE 75 - JOH CM ACTIVATIONS WHEN FLO COMES INTO VIEW AND DUMPS .........................................135 FIGURE 76 - FLO COOPERATION BY PLANNING NETWORK ..........................................................................137 FIGURE 77 - TYPICAL TRAJECTORIES DURING COOPERATION BY PLANNING ...............................................138 FIGURE 78 - FLO DURING A LOCATION LABELLING, WHEN FOLLOW IS INTERRUPTED .................................139 FIGURE 79 - JOH DURING LOCATION LABELLING, WHEN FLOSERVO IS INTERRUPTED ................................140 FIGURE 80 - PERFORMANCE OF LAYERED CLEANING SOLUTIONS ...............................................................141

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Chapter 1 I NTRODUCTION ´%HJLQDWWKHEHJLQQLQJµWKH.LQJVDLGJUDYHO\´DQGJRRQWLOO\RXFRPHWRWKH HQGWKHQVWRSµ  ²/HZLV&DUUROO

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his research was undertaken with the objective of gaining knowledge about multi-robot cooperation. To build a multi-robot system requires knowledge about building single robot systems. Having no previous experience in robotics, and only a scant introduction to Artificial Intelligence (AI), I quickly discovered that my expectations of robotic technology were vastly over-estimated. The immediate research focus shrank from ‘how do I build a cooperative multi-robot system’, to ‘how do I get a robot to do anything that seems intelligent’, and ‘how should the architecture be organised’, and so on. At this point, the decision was made to begin at the beginning. This thesis is the result of a re-examination of intelligence and intelligent cooperation, with the aim of insight into how intelligent cooperative systems may be constructed. Although mechanisms for cooperation and mechanisms for constructing intelligent robots in general are considered separately within the field, such an artificial divide may prove detrimental. The need to engineer a multi-robot system required a suitable control architecture as a framework for implementing both single and multi-robot systems. This search led to research into control architectures in general, into cooperation in natural systems and ultimately to an examination of the philosophy underlying the synthesis of complex systems.

1.1 OVERVIEW Most of the research into cooperative systems to date has concentrated on how to obtain desired interaction dynamics between agents. The two main approaches have been dubbed collective robotics and cooperative robotics. Collective robotics is characterised by distributed control of homogeneous robot teams. The desired collective dynamic is obtained as an emergent property of the interaction behaviour designed into each robot (eg. [Jin et al., 1994]). The approach is usually behaviour-based. The types of tasks implemented take inspiration from eusocial insect societies, such as ants. Many studies of the effect of communication in collective systems have been conducted. Cooperative robotics covers the rest of the field. It often involves systems of heterogeneous robots and usually employs either central control or a mix of central and distributed control. Of the systems employing distributed control of behaviour-based robots, only emergence of the Introduction

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cooperative dynamic has been utilised (eg. [Parker, 1994b]). However, the cooperative dynamic in most systems is achieved by planning. In these cases, the planning is often classical-AI based and utilises inter-robot communication. To our knowledge, there has been no work investigating distributed planning of cooperative behaviour with behaviour-based robots. Few researchers consider cooperative planning as an extension of the action selection problem facing individual agents. The cooperative robotics community often places an implicit artificial divide between planning the behaviour of a single agent and planning of group behaviour. Schemes have been proposed to modify the plans of a single agent to accommodate the global goals, for example, by plan merging or negotiation (eg. [Sycara, 1994]). The collective robotics community is not faced with this problem because the collective behaviour is not planned, but emergent. One objective of this research was to develop a behaviour-based architecture capable of planning both the actions of a single agent and of a cooperative system in a homogeneous manner. The basic rationale is – if the interaction between behaviour elements within an agent can accomplish action planning, then the interaction between behaviour elements among agents can accomplish cooperative action planning. The only difference is the possible mechanisms of interaction. Within an agent, behaviours may interact both via their effects on the environment and by direct information passing. Behaviours among agents can only interact via their effects on the environment, or possibly via explicit inter-agent communicative acts. In essence, just as with collective robotics, we consider the dynamics of the cooperative system as a whole, but our aim is to realise a truly distributed situated planning system. If robots are to communicate, an obvious question is ‘what should they talk about?’. Humans are among the few animals to communicate using symbols, and the only animal to use symbols extensively. Some AI researchers have built systems that reason in a limited way by manipulating tokens. As they claim these systems to be symbolic, there seems to be some variability in the use of the term ‘symbol’. We found it necessary to delve into the evolution of language and symbolic thinking in humans, to obtain a clear idea of what we really mean when talking of ‘symbols’. Armed with this knowledge, we can avoid many of the common pitfalls associated with mistaking tokens for symbols and employing anthropocentric categorisations. In the process of investigating cooperation schemes employed in biological systems, the research was led to an examination of what is meant by ‘cooperation’ in general. Being a word, ‘cooperation’ is a human symbol for a diverse class of behaviour observed in humans and animals. The contexts of animal and robot behaviour are very different. Consequently, there are differing circumstances when cooperative is appropriate. We identify under what circumstances robot cooperation is beneficial by examining why biological systems exhibit cooperation. With this in mind our Architecture for Behaviour Based Agents (ABBA) was designed and implemented. ABBA provides the basic substrate for action selection within the agent. It has been shown that communication can improve performance in multi-agent systems. Intuitively, explicit high-level planning of cooperative action should also afford performance gains. In keeping with the Behaviour-based philosophy, it is not possible to provide support for

An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

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distributed cooperative planning utilising two-way communication at the architecture level. This is because any communication between agents must be strictly grounded in the perceptualbehaviour space of the agents. Hence, the nature of communication will depend on the context, task and agent competencies. This is particularly constraining in the case of heterogeneous robots. The specific mechanisms for cooperation using communication were developed above the ABBA framework and within the context of a concrete task. As a proof of concept, ABBA and the cooperation mechanisms developed were used to implement a cooperative cleaning task. The task specification was deliberately contrived such that, to be accomplished using our two Yamabico mobile robots, a solution would require cooperation. As most robot designers realise, due to the current limitations of sensor technology and perceptual processing, it is very difficult to realise high-level behaviour that is also robust. Many real-world tasks can be achieved in a number of ways. There is usually a trade-off between performance and reliability. The most robust implementation of the cleaning task does not involve communication or high-level planning; however, better performance is achieved when employing them. One approach to overcome this trade-off is to layer sophisticated, but unreliable, behaviour above simpler reliable behaviour. The failure of high-level behaviour will not cause the failure of the system to complete its task, but only a temporary reduction in performance. Four layers were implemented for the solution of the cleaning task, each building greater capabilities upon the previous. The simplest scheme – emergent cooperation – takes the collective robotics approach, where the behaviour of each robot is designed so that the combined effect solves the problem. There is no awareness in the robots of each other, no communication, and no map learning. The next layer – cooperation with observation – adds the capacity for one robot to visually identify and track the other. In combination with limited reasoning about the actions and intentions of the other, this provides performance-enhancing information when in visual range. Above this is layered the cooperation by communication scheme. This enhances the knowledge of one robot about the actions of the other by the explicit communication of state information between them. The final layer – cooperation by planning, considers communicative acts as behaviour that affects the environment – the other robot. It adds a map-learning and navigation capability in conjunction with limited predesigned communicative dialogues. The communication is grounded, although the robots have different sensory-motor systems. This is achieved by a location labelling behaviour in each robot. When they are within visual and communication range, an arbitrary label is selected for the current location by one robot and communicated to the other. Each robot associates its current location, using its own learnt map representation, with the label. Further behaviour communicates dump locations for piles of litter in terms of these known locations symbolically, and navigates to them. Navigation was achieved by developing a grounded representation that incorporates topological and spatial information for concurrent localisation and map learning within ABBA. Since the representation is in terms of the perceptual-behavioural repertoire of the robot, the structure of the map is meaningless if directly transferred from one robot to another. In essence, in this layer, communicative acts are planned by ABBA along with all other actions, to realise distributed joint planning. The joint planning mechanism was modelled on primate cooperation.

Introduction

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In summary, the contributions of this research are (in order of presentation): •

An in-depth look at ‘cooperation’ – its meaning in the animal and human context, history and mechanisms.

•

A quantitative argument showing under which conditions multi-robot systems can be employed to benefit as an alternative to monolithic single robot solutions.

•

An architecture (ABBA) embodying a homogeneous action selection mechanism capable of developing plans including all types of behaviour – from low-level self-preservation behaviour, to navigation, cooperation and communicative dialogue.

•

The development of a unique proportional whisker sensor to realise close wall-following behaviour.

•

The implementation of visual behaviours for identification and tracking of another robot, and locating ‘litter piles’.

•

A mechanism for concurrent localisation and map-learning, combining both topological and spatial information, keeping within the behaviour-based approach.

•

Development of mechanisms for cooperation based on primate models involving a grounded communication system, where symbols are grounded by demonstration using vision.

•

An approach to increasing cooperative system performance without the trade-off in reliability in a multi-robot system by layering of increasingly sophisticated behaviour.

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Demonstration of the ABBA architecture and cooperative mechanisms by the successful implementation of a cooperative cleaning task.

1.2 OUTLINE In Chapter 2, we investigate the historical background behind the development of the classical AI model of robot architecture. We trace the ideas back to cognitive psychology, and ultimately to ancient philosophical ideas. The consequence of these philosophical ideas is explored. In particular, the notion of a ‘symbol’ – often ill defined in the AI literature – is placed on firm ground by following the evolution of the first symbolic representation in humans. This leads to a thorough understanding of the grounding problem and the pitfalls of reductionism and anthropocentric symbol systems. Finally, we argue that behaviour-based design is also subtly influenced by the application of anthropocentric interpretations to linguistic labels during design. The ideas developed are crucial to the understanding of why various design decisions described in the thesis were taken. Our account is based in fact drawn from a variety of disciplines and notable researchers. Chapter 3 first outlines how cooperation can benefit robotics. In particular, we contribute a rationale for the use of cooperative systems in many applications where monolithic robots could be used – the benefits in increased reliability and reduced cost are quantitatively demonstrated. Before an architecture for cooperative control can be developed an understanding of ‘cooperation’ in necessary. This is elucidated by recourse, again, to An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

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biological and evolutionary accounts of cooperation and its mechanisms. Cooperation in selected contemporary robotic systems is discussed, and finally the lessons learnt up to this point in the thesis are summarised in the form of a set of important points. The Architecture for Behaviour Based Agents (ABBA) is introduced in Chapter 4. Before detailing ABBA, a specification for a cooperative cleaning task is given both to serve as a concrete implementation goal and to drive architectural design requirements. The ABBA architecture supports the design and implementation of cooperative systems without imposing any specific cooperation mechanisms. The architecture is presented in detail, including the action selection model and its implementation in terms of a spreading activation algorithm. The mechanisms by which ABBA is used to achieve navigation, cooperation and communication are also introduced. Next, a series of solutions to the cleaning problem are presented, each scaling up sophistication and efficiency by adding capability to the previous. The solutions draw on the knowledge of cooperation gained, and provide the basis for a layered implementation of the task. All of the specific behaviours developed for the cleaning task implementation using ABBA are described in Chapter 5. First, however, our robot hardware is detailed, including the development of a novel whisker sensor appropriate to our task. The implementation of ‘basic’ or ‘low-level’ behaviour components are detailed, including various visual and whisker centric behaviours. Next, some ‘higher-level’ mechanisms layered above ABBA are described that support purposive navigation using an integrated spatial and topological representation, deictic indexical reference and cooperative planning. Cooperative planning is realised in a distributed manner relying on local and partial global information. The only global information available is that obtained via a grounded task based communication dialogue. It is based on observations of the co-construction of joint plans by primates. Chapter 6 provides experimental results obtained through integration of all described behaviour into a layered implementation of the cleaning task. The system provides a proof of concept for the engineering of reliable cooperative systems within the behaviour-based philosophy capable of ‘high-level’ behaviour and sophisticated grounded representations. The performance is shown to scale with the addition of layers to the implementation. The final chapter, Chapter 7, concludes and speculates on future directions for research. A glossary of terms is provided in the back for reference.

Introduction

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major goal of this thesis is to develop an architecture suitable for the implementation of a cooperative multi-robot system. The concrete task we set ourselves to implement requires our system to perform ‘high-level’ behaviour, such as purposive navigation, map building, cooperation and communication. This chapter gives some background that will become important to understanding why various decisions were made during the design of ABBA. The classical AI philosophy is founded on the physical systems symbol hypothesis [Newell and Simon, 1976] that maintains that intelligence is of a symbolic nature. This has been challenged at length in the literature, by the behaviour-based school, connectionist research [Churchland et al., 1992] and more recently the dynamical systems theory approach [Van Gelder, 1995]. A thorough investigation of the basis of classical AI philosophy, and the criticisms levelled at it by the behaviour-based community, revealed a philosophy whose ideas can be traced back to early thinking about mind and reality. One goal of this chapter is to trace the reason why classical AI originally fell into the trap of using ungrounded symbol systems to argue that the so-called ‘Behaviour-Based’ approach suffers from related problems. Our investigation leads to the conclusion that it was due to the pervasive nature of dualistic thought throughout western culture and science in particular. The first section of this chapter traces the ideas behind classical AI, often implicitly embodied, to their historical origins. We have decided to use the behaviour-based approach. However, it seems that few behaviour-based researchers are tackling ‘higher-level’ behaviour with their systems. In addition, there seems to be a variety of notions about exactly what behaviour-based robotics is. We show that some of the problems plaguing contemporary robotics research are also due to subtle consequences of the all-pervasive ‘Cartesian dualism’. As we wish ABBA to support grounded symbol systems, the second section looks at symbols and representation in general from a biological perspective. In particular, we briefly examine why symbolic thought evolved in humans to give us an insight as to how they might be developed for a robot. The final section discusses some pitfalls of reductionism that stem from Cartesian dualism and misunderstandings about the nature of symbols. One specific pitfall is the natural tendency for humans to use anthropocentric categorisations in their design. There

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are circumstances where this is known to cause problems, for example by leading to frame problem [McCarthy and Hayes, 1969] due to ungrounded symbol systems. However, behaviour-based robot designers are using anthropocentric linguistic labels for design elements. While the designers know this, and feel there is no problem, we believe that there are some subtle implications that lead to problems. We explain and explore the implications.

2.1 THE SEEDS OF AI FROM 38000BC This section is the product of our investigation of the ideas behind classical AI philosophy. Rather than trace these ideas backward, this discussion starts, briefly, at 38000 BC with the first modern humans, and works forward to the present time. In each subsection, the ideas evident can be seen to influence subsequent thinking in the following subsection. We finish with classical AI and behaviour-based robotics.

Present

Behaviour Based Approaches

Classical Sense-Plan-Act Approach Evolutionary Psychology

Classical A.I.

~1925

Modern Science Quantum Mechanics / Chaos Theory / Dynamical Systems

Cognitive Psychology

0-1920A.D.

Traditional Science (reductionist)

~470 B.C.

Dualism (Socrates / Plato / Aristotle)

Dualist Religion (Christianity / Judaism / Islam)

Eastern Religions (physicalist) 3000 B.C. Mimetic evolution of ancient religious culture ~38,000 B.C.

Figure 1 - Relationships between some Human endeavours

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The diagram in Figure 1 shows a time line with the major epochs marked. The lines represent the influence of ideas from bodies of though into newer ones.

2.1.1 PRIMATES AND PANPSYCHISM Panpsychism is the doctrine that all objects in the world have an inner being. The term was popularised by the anthropologist Sir Edward Tylor in studies of the origins of aboriginal religion and beliefs. According to Tylor, panpsychistic philosophy developed to explain the causes of sleep, dreams, trances and the difference between a living body and a dead one [Tylor, 1958]. It is essentially a dualistic theory since it implicitly proposes two distinct kinds of ‘stuff’ – physical stuff and inner mental stuff. Dualistic thinking in general results from the natural tendency of minds to categorise (divide) in order to organise. Tool using Hominid primates first appeared on Earth about 3 million years ago. They probably had an awareness of the perspectives of others – called a theory of mind – as modern non-human primates do [Diamond, 1991; Wright, 1995]. This adds to a primate’s notion of self, a concept of self within an autobiographical social context. It seems likely that this ability, and the way they mourn their dead, implies a connection with a wide ranging phenomenon known as panpsychism. Modern Homo sapiens appeared on Earth around 38000 BC. Their Neanderthal ancestors were still the dominant primate and had developed simple language, art, tools, fire and sophisticated hunting skills almost 40000 years earlier. Little can be known about the religious beliefs of Neanderthal and early Homo sapiens, however their beliefs are likely to have been some form of precursor to panpsychism. Hence, it is possible that essentially dualistic thinking is deeply embedded into our primate ancestral cultures. Although Neanderthal culture was quite sophisticated, in the next 35000 years Humans (Homo sapiens sapiens) significantly enriched this culture to include, among other things, a more sophisticated religion – to which we now turn.

2.1.2 EASTERN MYSTICISM The dominant philosophy for about 35000 years was that which formed the basis for what we now recognise as the eastern religions – Buddhism, Hindu, Taoism, Muslim. The basis for all these religions is physicalist – the central doctrine being the belief in a unity of all things 1. Human culture had reached a point where we recognised the artificiality of our linguistically categorising and dividing minds – in the sense that these reflect divisions, not of the world itself, but of useful ways to divide it for human purposes. What we will subsequently refer to as anthropocentric categorisations. For example, the Yin-Yang symbol ( ) represents the dual nature of true and false, or good and bad. It shows the recognition that ‘good’ and ‘bad’ are only human designations for categories of behaviour that actually lie on a continuum, and only have value in relation to 1 Although many eastern religions now believe in many Gods and deities, they were historically understood to be no

more that human designations for different aspects of the one unified reality [Capra, 1991]. This originally monoist position has shifted in some modern forms of eastern religions due to western influence, which now view such deities as having an independent ‘spiritual’ existence – they have become dualist. An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

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interpretation by humans. ‘cooperation’.

As we will see in Chapter 3, we can say the same about

2.1.3 WESTERN CULTURE AND THE CARTESIAN DIVIDE Around 470BC, the famous Greek philosopher Socrates2 popularised a philosophy that emphasised rational argument as the path toward knowledge. Thus, the path toward Western culture was begun. He greatly influenced his pupil Plato who believed that the world can be understood in terms of his theory of forms [Moravcsik, 1992]. This theory is dualistic as it postulates that in addition to the physical world there is another ‘kind of stuff’ – forms that exist in the Platonic realm. Plato’s student Aristotle followed in the tradition by postulating that the universe is made of two spheres [Aristotle, 350BC]. The central sphere composed of the four elements, earth, air, fire and water, and the heavens from aither. Greek philosophy influenced René Descartes in the 17th century, who continued dualistic thought. Descartes proposed that the mind is independent of the body and only intermittently causally interacts with the material world [Descartes, 1637]. This substance-dualism causes many problems with explaining the interaction between two different ‘kinds of stuff’. This has become known to philosophers as the mind-body problem ([Cornman et al., 1982] Chapter 4). However, the main effect of Descartes’ ideas on the subsequent development of western though was, as Wheeler puts it, “to open up an explanatory divide between mind and world, according to which fundamentally ‘internal’ subject relates to an ‘external’ world of objects via some interface.” [Wheeler, 1996]. It is this, Cartesian divide, now discredited, which has carried through into the philosophies of traditional science [Damasio, 1994]. The development of western thought is presented in great detail by [Tarnas, 1991]. It is a relatively recent development that the State, the Church and Science have become separate entities in western culture. Science and the Church share a common goal – to understand the world of everyday life for ultimately improving quality of life. Both Science and the Christian Church took elements from dualist philosophy. As Gatherer states, “Despite the uneasy relationship between Christianity and science over the last 400 years, a case can be made that both are derivatives of Platonism.” [Gatherer, 1997]. Platonism’s influence can be seen in The Old Testament of the Bible as used by Christianity and Judaism, as well as Islam. Although science maintains a strictly physicalist position it still adopts a recognisably Cartesian attitude. This Cartesian attitude has affected the field of Cognitive Psychology.

2.1.4 COGNITIVE PSYCHOLOGY Cognitive psychology is pervaded by such terms as ‘conscious’ and ‘subconscious’. This implicitly proposes the existence of a dividing point in the mind that separates subconscious thoughts from conscious ones. The conscious part of the mind is usually assumed to be the place where experience ‘all comes together’ into an awareness. This seems to require an internal observer to view the play unfolding in what Dennett calls the Cartesian Theatre. As Dennett says: 2 Socrates did not write anything, he is only known through the writings of Plato.

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“Perhaps no one today explicitly endorses Cartesian materialism. Many theorists would insist that they have explicitly rejected such an obviously bad idea. But […] the persuasive imagery of the Cartesian Theatre keeps coming back to haunt us – laypeople and scientists alike – even after its ghostly dualism has been denounced and exorcised.” – Daniel C. Dennett, Consciousness Explained, pp107 [Dennett, 1993]. Cognitive science is a combination of representationalism and computationalism. It essentially postulates an internal representation, or model, for the world which ‘plays’ an abstraction of sensory experience. The internal observer of this Cartesian theatre is a central decision-maker that computationally reasons based on the model to determine action. This model from cognitive science was adopted by the Artificial Intelligence (AI) community and eventually led to the ‘sense-plan-act’ paradigm of robotics.

2.1.5 CLASSICAL ARTIFICIAL INTELLIGENCE The central thesis of classical AI is the physical systems symbol hypothesis – that “Natural cognitive systems are intelligent in virtue of being physical symbol systems of the right kind” [Newell and Simon, 1976]. Placing such a high importance on symbols, or language, is a direct result of the influence of cognitive psychology. In particular, it provided the idea of a ‘concept’ – a linguistic symbolic label for the representation of an abstracted idea [Brady, 1984]. Hence, the problem of how to represent the world within a computer was addressed by creating systems that model the world in terms of linguistic labels of abstract ideas and then manipulate them according to rules – perhaps the rules of logic. These were called symbol manipulation systems. In addition, classical AI developed in parallel with computer science and the introduction of high-level programming languages. Essentially a high-level language allows the programmer to give linguistic labels to stored data structures – seemingly the perfect tool for the implementation of symbol manipulation systems. There are some flaws in the conception that we will explore in the next major section of this chapter (section 2.2). Essentially these systems manipulate tokens that have no intrinsic meaning. The tokens only become symbols – gain meaning – when they are grounded in a physical context. We will see that there are actually two possible groundings when such systems are used in the context of robotic systems – one in the designer and one in the embodied robot. Overlooking either can cause problems.

2.1.6 ROBOTICS Roboticists are faced with the task of engineering machines that gather information about their world via sensors, reason and effect action via actuators. The natural approach was to use the symbolic manipulation systems developed by AI. Hence, it imported the Cartesian dualistic philosophy that we have traced from the ancient Greeks. The ‘only’ other requirement then, was to develop a perception system to abstract sensory information into symbols and a mechanism for turning symbols into actuator commands. Hence, the sense-plan-act paradigm was born – also known as the classical AI approach to robotics (see Figure 2).

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Robot Plan Reasoning

Sense

Motor program elaboration Perceptual hierarchy

Act

Actuator outputs

Sensor inputs

World

Figure 2 - The ’Sense-Plan-Act’ paradigm

In the early 1980s, many robotics researchers began to realise that the AI approach to robotics wasn’t living up to expectations. The approach had limited success in simple artificial environments, but failed to be scalable to real environments where the systems became brittle. The most cited example is the Stanford Cart, which spent so much time computing the Planning stage, that its visual reasoning was disrupted by the changing shadows due to sun movement [Moravec, 1980]. Around 1986, Brooks proposed another way to look at the problem along with a new architecture, called the subsumption architecture, that embodied his philosophy [Brooks, 1986]. We believe the inspiration came from a number of sources. Firstly, the architecture provided an implementation aid in the form of layering (see Figure 3). If each layer can be debugged before adding another, which will not disrupt the functioning of the lower one, then the layers can be developed independently. This makes the design task considerably more manageable. As we will see in Chapter 3, this layering is evident in biological systems for a similar but different reason. Behaviours

Layer 2 Layer 1 Sensors

Layer 0

Actuators

Figure 3 - Subsumption architecture3

3 Reproduced from [Brooks, 1987a].

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motor control

task execution

planning

modelling

sensors

perception

In addition, around this time there were researchers recognising that an iteration of the ‘sense-plan-act’ cycle results in response times that are too long for many robotic tasks. Architectures that allow for processing at different time resolutions were proposed (eg. NASREM [Albus et al., 1987]). Brooks incorporated similar ideas into the subsumption architecture by proposing that control architectures should be composed of vertical taskachieving modules, rather than the traditional horizontal decomposition into functional modules (see Figure 4). The solution to the cleaning task using ABBA will utilise such a layering.

actuators

... build maps explore actuators

sensors wander avoid objects

Figure 4 - Classical (top) verses Behaviour-based (bottom) decomposition4

This brought about a paradigm shift in much of the community and Brooks’ name is now synonymous with the, so called, behaviour-based approach. The behaviour-based approach is now seen as an overall philosophy rather than being associated only with Brooks’ subsumption architecture. The emphasis is on embodied, situated agents that work in real-time and explicit representations are avoided [Brooks, 1991; Matariþ, 1992a]. Hence, the approach rejects the Cartesian divide propagated by cognitive science and AI.

2.1.7 SUMMARY What we have seen is the development of dualistic thought and how it has been propagated through Western culture into traditional science. This in turn leads to the necessity of Dennett’s Cartesian Theatre in cognitive psychology, classical AI and finally to the application of this to robotics – the ‘sense-plan-act’ paradigm. We have alluded to some of the problems that this conception faces, some of which have been elucidated in the literature, but they cannot be fully understood without the perspective on symbols and representation presented in the following section. What we will argue, is that the behaviour-based philosophy of building situated, embodied agents only partially addresses these problems.

4 Reproduced from [Maes and Brooks, 1990b].

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2.2 SYMBOLS In this section, we look to evolutionary biology and neurology for a concrete understanding of symbolic representation – only then will we be in a position to understand the problems with classical AI and the behaviour-based approaches. The types of reference upon which symbols are built are specifically supported within the ABBA architecture. Before we can see how humans represent the world in terms of symbols, we need to investigate various aspects of animal and human minds – consciousness, intentions, plans, selfawareness and language. To understand symbolic representation a summary of the evolution of symbols in hominid societies is given. A detailed explanation is beyond the scope of this thesis; hence it is necessarily brief and misses some important points. A thorough definition of terms such as ‘learning’, ‘reasoning’, ‘representation’ and ‘behaviour’ is not attempted for the same reason. It is important to note that we believe that these terms actually refer to a single underlying process of cognition – each emphasises a different aspect of the process. Learning emphasises the longer-term changes in the cognitive process over time, while reasoning emphasis the short term time evolution. Representation is concerned with the structural organisation of the process at some level, and behaviour its outward expression. We believe, due to the arguments against reductionism presented below, that each cannot be separated out and usefully studied in isolation. Our argument includes elements derived from a condensation and integration of works by a number of researchers, including but not limited to: Wright, Diamond, Dawkins, Dennett, Deacon, and Tarnas [Wright, 1995; Diamond, 1991; Dawkins, 1996; Dennett, 1993; Deacon, 1997; Tarnas, 1991].

2.2.1 REPRESENTATION Neurologists distinguish between three types of reference, or levels of representation – iconic, indexical and symbolic [Deacon, 1997]. Iconic representation is by similarity to stimulus, in terms of salient sensed features. Indexical reference is correlation between icons. All animals are capable of iconic and indexical representation to varying degrees. Even insects probably have limited indexical capabilities. For example, an animal may learn to correlate the icon for smoke with that for fire. Hence, smoke will come to be an index for fire. The third level of representation is symbolic. A symbol is a relationship between icons, indices and other symbols. It is the recognition and representation of a higher-level pattern underlying sets of relationships. The only way to learn a symbol in terms of indices is to be presented with many indexical associations until the underlying pattern is grasped. Once this occurs, that symbol allows a degree of independence from the associations that generated it. Although a symbol can still only be interpreted by being grounded back to these iconic and indexical references, it can be used in other contexts to refer to similar relationships between other sets of associations. Chimpanzees have been taught simple symbol systems but they had great difficulty learning them [Gill, 1997].

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Some symbols are discovered as a direct result of our physical interaction with the world. These symbols are metaphors for spatial and bodily relationships. For example, the idea of ‘more’ may be grounded back to concepts such as further away, heavier, higher, bigger and similar empirically derived concepts. Representations also categorise the world in ways that are appropriate for the uses to which they are put – they are task oriented. This fact has been recognised by psychologists who use the term affordance ([Leckie-Tarry, 1995] also see discussion in [Fodor and Pylyshyn, 1981]). Objects in the world are identified with what can be done with them or what they are good for – their affordances. Therefore, animals perceive the world in task relevant terms. Representations in biological systems are also distributed and redundant. One consequence of this is that multiple different representations for the same piece of information may contradict each other [Edelman, 1994; Edelman, 1998]. Such contradictions are not necessarily reconciled unless that particular piece of information is required. For example, suppose you are looking at an object in your hand. There will be independent representations for elements of its shape in brain areas corresponding to different sensory modalities – each appropriate for particular tasks. If, for example, the object in your hand has a feature that is rough to the touch, but looks smooth, then some elements of the visual and tactile representations may be contradictory. If you are given the task of reporting if the feature is rough or smooth, then a decision process will be forced to reconcile the two contradictory pieces of information. There is evidence that such decisions are made in a competitive manner (as described by ([Calvin, 1996] chapter 6), see also [Townsend and Busemeyer, 1995]). The time taken to make the decision will depend on how similar the levels of reliability of each piece of information were considered to be – the more similar, the longer it takes. If it takes long enough, it may recruit the motor system for a closer inspection. This type of ‘lazy’ reconciliation allowing contradictory beliefs to be held is a common feature of biological cognition. This brings us to the following important point to which we will later refer.

1. Symbols have meaning by being grounded We have seen that symbols have meaning by being ultimately grounded to indexical and iconic representations. Hence, they represent categorisations that are specific to the physiology, behaviour and tasks of the agent in which they are grounded. In humans, we call these anthropocentric categorisations. The ABBA architecture will directly support iconic reference in the form of feature detectors and indexical reference through association learning. ABBA will make decisions, in the form of action selection, in a competitive manner. While representations are not dictated at the architecture level, our implementation of the cleaning task will utilise task specific representations – in terms of the affordances of the robot. We do not believe that attempting to design symbols into a robot will yield useful results, but instead we should endow robots with mechanisms for acquiring appropriate symbols during their ‘development’.

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Before we can appreciate why there are problems with the ‘sense-plan-act’ paradigm and ungrounded symbols lurking in behaviour-based systems, we must briefly examine how symbols are acquired. A good illustration is provided by contemporary theories of why symbols evolved in hominids. This is discussed below.

2.2.2 LANGUAGE ´:KHQ,XVHDZRUGµ+XPSW\'XPSW\VDLGLQUDWKHUDVFRUQIXOWRQH´LWPHDQV MXVWZKDW,FKRRVHLWWRPHDQ³ QHLWKHUPRUHQRUOHVVµ ²/HZLV&DUUROO7KURXJKWKH/RRNLQJ*ODVVFK  

Most animals have primitive communication skills, but human language far surpasses that level of sophistication. Human language is hypothesised to have evolved over a relatively short period in evolutionary terms, perhaps beginning as little at 350,000 years ago – around the time of homo erectus. This is not long enough for significant genetic changes to occur in the development of the hominid brain. Consequently, it was language it-self that has done most of the adapting to hominid brains [Deacon, 1997]. The following section answers the question “Why was symbolic representation necessary?”. This in turn illustrates that, in addition to being grounded, symbols are always acquired in order to solve specific problems, not ‘hardwired’ or the consequence of any general mechanism for abstraction without purpose. We will demonstrate symbol acquisition in a similarly specific context using ABBA.

2.2.2.1 A symbolic solution to a socio-ecological dilemma Early stone tools indicate a shift in hominid diets to include more meat, which was an alternative food source that substituted for their preferred food during lean times. Meat is preferentially available to males, since females cannot hunt due to their ongoing reproductive burdens. From an evolutionary standpoint, there is little benefit in a male providing meat to a particular female and her offspring given the uncertainty of paternity. The dilemma is – that in order for meat to become a subsistence food, it must reliably be supplied to those females least able to obtain it – those with children. Males will only reliably provision females if they can be certain they are provisioning their own progeny. However, paternity is particularly uncertain if males spend much time away hunting. This is a catch twenty-two situation. The only solution to this dilemma is the utilisation of a social structure that guarantees unambiguous and exclusive mating. Unlike a pair bond in a species where the couple remains isolated, establishing an exclusive sexual bond in a social setting is more than a relationship between two individuals. It is also a contract involving other members of the group. A promise from other males about their sexual conduct and promises from other members of the group to behave toward individuals that take advantage of un-condoned sexual opportunities in a way that makes such behaviour socially too expensive. This type of social contract – marriage – can only be represented symbolically.

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2.2.2.2 The frontal cortex takeover Given that ‘marriage’ and other early symbolic concepts were useful to hominid societies, there was strong evolutionary selection for individuals with a better ability to subjugate their normal strong stimulus response behaviour to alternatives available due to symbolic understanding. For example, males that are able to repress sexual advances to married females. These early Hominids had considerably less frontal, or voluntary, control over their responses than modern humans do. Modern Chimpanzees display a similar lack of voluntary control. The following example illustrates that we modern Humans have the same difficulty, but to a much lesser extent. Try to read aloud the colours of the each of the words in Figure 5 quickly.

Blue Orange Red Green Black Purple Yellow Red Orange Yellow Blue Black Green Red Purple Yellow Red Orange Black Black Green Purple Blue Green Figure 5 - Conflict between Colour and Words5

A much greater effort is required to inhibit the tendency to read the word instead of saying its colour. As evolution selected for a greater ability to control behaviour according to symbolic associations, the effect was to bring many previously involuntary functions under voluntary control – such a vocalisation. The difference between homo erectus brains and modern human brains is not one of total size, but only a slight quantitative change of frontal cortex enlargement relative to other areas [Deacon, 1997]. Since the ‘wiring’ of a brain occurs during development, by competition between areas for axon connection recruitment due to sensory input, the main effect was to place the whole brain under greater control of the frontal cortex6. Voluntary control of vocalisation paved the way for the use of sounds as tokens for symbols (words) rather than the more cumbersome external physical signs. Because of the difficulty for early hominid brains to learn symbolic relationships, complex social and ritual support was likely needed to aid the learning, and hence transmission, of this early symbolic culture. Interestingly, many physical signs remain in use today, such the wedding ring. Once the symbolic ball was rolling, the sophistication of human culture exploded within a very short time. This is due to a very different form of evolution that is Lamarckian in nature with respect to individuals – based on inheritance of acquired traits [Lamarck, 1809] – rather than Darwinian. Symbols were selected for their ‘learnability’ during transmission from one generation to the next. Hence, culture and language were crafted into a symbol set that has the best chance of being learnt and passed from one generation to the next. This type of evolution 5 Reproduced from [Torok, 1998] pp23, who sourced it from The Exploratorium, San Fransisco (originator

unknown). 6 There is strong evidence linking the frontal cortex with symbolic representation. The type of neural computation

the frontal cortex is adapted for was initially suited to voluntary control of the motor system [Deacon, 1997]. It has direct cortical connections to the pre-motor and motor cortices. Locomotion is largely voluntary in all animals. An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

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of symbol systems has been dubbed memetic evolution by Dawkins [Dawkins, 1996]. The study of meme pools is termed macromemetics. Macromemetics is placed in the context of historic and modern western philosophy by Gartherer [Gatherer, 1997].

2.2.2.3 Summary Symbols are a powerful form of representation almost uniquely used by humans. Through the expansion of the frontal cortex and other biases in learning brought about by the co-evolution of language and the human brain, humans have become skilled at learning and using complex symbol systems, or meme pools. Since symbols are relationships between other symbols, indices and icons, the interpretation of a symbol always involves grounding it back to these references. These references in turn are a result of the learning history, or development, of the individual and are categorisations of the world tailored to the tasks, or affordances, for which they are employed – anthropocentric categorisations. Symbols are acquired through experience as specific needs arise during the normal social interaction of human communities.

2. Most representations are learnt Although many iconic representations and some indexical representations may be ‘hard-wired’, most indexical and all symbolic representations are acquired through learning and behaving. Learning should be an integral part of any robot architecture – not a retrofitted after thought. Consequently, learning is an integral part of the ABBA action selection substrate. Symbolic communication requires the listener to re-ground a symbol in terms of his or her own references for interpretation. This necessarily implies that for successful communication, culture has evolved to homogenise the contextual symbolic and indexical supports to which many symbols are grounded. Therefore, any artificial agent must also learn our cultural symbol system before being able to successfully interpret and communicate using human symbols. Failure to appreciate this by artificial intelligence researchers results in a number of invalid assumptions being made, some of which are explored below. These ideas will be critical for development mechanisms for the implementation of the concrete cleaning task. They will particularly play a role in the grounded representations and communication required between the robots. We are now in a position to examine the subtleties of unwanted anthropocentric symbol grounding in classical AI and behaviour-based systems.

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2.3 THE PITFALLS OF REDUCTIONISM ´,URQLFDOO\WKHYHU\DVSHFWRIEHKDYLRXUWKDWVFLHQWLVWVHVSHFLDOO\SV\FKRORJLVWV DQGELRORJLVWVXVXDOO\WU\WRDYRLG²LQVWDELOLW\RIPRWLRQ²WXUQVRXWWREHWKH NH\JHQHULFPHFKDQLVPRIVHOIRUJDQLVDWLRQµ ²-$6FRWW.HOVR'\QDPLF3DWWHUQV

There is currently a quiet revolution of thinking occurring across diverse areas of scientific inquiry ranging from quantum physics to neurology, evolutionary psychology and ethology [Jackson, 1994]. Specifically this is the realisation of the very immediate shortcomings brought about by reductionist thinking. Many scientists are unaware they are implicitly applying reductionist thinking, because it’s a tradition (meme) that’s being propagated through higher education institutions. Scientists in the business of ‘reduction’ have made spectacular progress. Brains can now be ‘explained’ in terms of neurones, neurones in terms of particles, particles in terms of atoms and atoms in terms of quarks. However, we must ask how useful the term ‘explained’ is when used in this context. This method of decomposing systems into ever-smaller parts to examine them is evidently useful if we wish to know the hierarchical structure of a system. However, it is all too often assumed that once we have understood the component parts of a system, we can easily understand the interaction of these parts as a whole – this is not true. For example, until recently there was no proposition of how the properties of quantum mechanics might give rise to classical physics. The problems with reductionism have been well explored in the literature. For an examination of “Reductionism and Other Isms in Biology” see [Thomson, 1993] Chapter 14 and also [McLaughlin, 1992]. There is now beginning a realisation in the scientific community that a system really is more than the sum of its parts. This is evidenced by the relatively recent resurgence of interest in non-linear dynamics of complex systems, chaos theory, dissipative systems and related disciplines. These theories can explain how emergent properties arise in systems of large numbers of interacting units through self-organisation. As Mandik states, “Sloganisticly, a system exhibits emergent properties when those properties are more than the sum of its parts’ properties” [Mandik, 1997]. Cartesian dualism is an instance of reductionist thinking (see [Hardcastle, 1992]).

2.3.1 ANTHROPOCENTRIC CATEGORISATION The term ‘anthropomorphism’ generally refers to the attribution of human characteristics to non-human objects. By anthropocentric categorisation then, we mean the division, or categorisation, of the world into categories appropriate to human requirements. As we saw above, classical AI borrowed the idea from cognitive science that intelligence consists mainly of symbol manipulation. The adoption of symbol manipulation for the ‘sense-

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plan-act’ paradigm in robotics required the linking – grounding – of these symbols to world perceptions and actions. These systems were often designed using a ‘top-down’ approach, whereby the tokens being manipulated by the central planner were understood to have an anthropocentric interpretation, and then the perception system was designed to ground them to support this interpretation. The tokens were often linguistically labelled elements of a computer program, written in a high-level language, that implemented the system. “Part of the danger in current computer metaphors comes from our tendency to call typographical characters ‘symbols’, as though their referential power was intrinsic” [Deacon, 1997] pp443. This approach seemed natural because, as Deacon also says, “We [humans] tend to apply our one favoured cognitive style to everything […] we cannot help but see the world in symbolic categorical terms, dividing it up according to opposed features, and organizing our lives according to themes and narratives.” [Deacon, 1997] pp416. This approach violates point 2 – ‘Most representations are learnt’, as the grounding of the symbols is engineered. Therefore, there are two possible groundings, or meanings, for the tokens in the system. The first is the engineered grounding via the perception system of the agent. The second is the grounding the designer applies to the linguistic label associated with the token – an anthropocentric grounding within the mind of the designer. The physiology, sensors and behaviour of contemporary robotic systems are impoverished and very different from that of humans. Hence, the two meanings are unlikely to accord, despite the best efforts of the designer.

3. Anthropocentric symbol systems don’t work for robots The inequality of the two meanings assigned to tokens within the agent – the agent’s and the designer’s, implies that the results of ‘reasoning’ by the planner may yield conclusions that are ‘wrong’ in the interpretation of the designer. As the designer is responsible for engineering the mechanism that elaborates plans into actions – the actions of the robot may also seem to be ‘wrong’. For this reason, we have designed the solution to the cleaning task with ABBA using only representations that arise naturally from the behaviours and task context of the system. The next section reveals why most behaviour-based systems only partially avoid the pitfalls of anthropocentric symbols in their designs.

2.3.2 BEHAVIOUR-BASED ROBOTICS The problems that arise from deliberate use of human symbol systems in artificial agents – anthropocentric categorisations – have been well documented in the literature. One such problem is termed the frame problem (see [Ford and Hayes, 1991; Pylyshym, 1987]). The importance of situated and embodied agents has been actively espoused by members of the behaviour-based robotics community, in recognition of these problems, for many years (see

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[Brooks, 1991; Matariþ, 1992a; Pfeifer, 1995; Steels, 1996] for a selection). This hypothesis is called the physical grounding hypothesis [Brooks, 1990]. By being embodied, a robot has a particular physiology that directly determines the structure of iconic representations, and thus indirectly influences indexical and symbolic ones. By being situated in the world, a robot has real-time sensory and behavioural tasks to perform, which also directly determine what representations and processes are appropriate. Adherence to these criteria attempts to ensure that the representations used by the system, symbolic or otherwise, are appropriate to the sensors, behaviour and affordances of the agent, rather that being anthropocentric. While we believe this is a large step in the right direction, if we look at many actual behaviour-based systems, we can find anthropocentric symbols causing problems. Many design diagrams for behaviour-based systems show boxes that represent component, or basic, behaviours that are interconnected with lines representing the flow of information. The behaviours typically have linguistic labels such as ‘avoid-collisions’, ‘pickup-object’, ‘go-home’, ‘find-object’, etc. Although the behaviour may be implemented using schema or state machines, for example, the boxes denote abstraction barriers – as Brooks calls them [Brooks, 1990]. Although the linguistic labels are not available to the system, serving only as a design aid, their meaning as interpreted by the designer will influence the system design. The result is that the boxes really constitute a division of the behaviour space of the agent by the designer – an anthropocentric categorisation of behaviour. As representations should be grounded in the sensory and behaviour space of the robot, and learnt, this violates both points 2 and 3 – (‘Most representations are learnt’ and ‘Anthropocentric symbol systems don’t work for robots’). Practically, this problem does not have a great impact in the ‘toy’ systems that constitute the current state-of-the-art robotic systems. However, we believe failure to observe the following point results in an approach to system design that will not scale in future.

4. Most behaviour is learnt Little animal behaviour is innate. Almost all behaviour is learnt with a small amount of ‘pre-wired’ behaviour or behavioural biases to bootstrap learning [McFarland and Bösser, 1993]. A number of robotics researchers have recognised this problem, either implicitly or explicitly (eg. [Steels, 1996d; Stein, 1997; Gaussier et al., 1998]). For example, in Steels’ use of his process networks he resists labelling any set of processes as a behaviour. Any process may play a role in a number of difference behaviour systems ([Steels and Brooks, 1995] Chapter 5). Although some proposals for a solution to this problem have been made, we are far from a consensus about how agents can be built to avoid this pitfall. Steels proposes that sets of additional constraints, in the form of recurrent patterns, on how process networks are assembled may help. Only a small number of recurrent patterns have so far been identified. Unfortunately, there is no method for constructing agents that avoids this pitfall while meeting our need to implement a cooperative system capable of supporting higher-level

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behaviour. Therefore, our cleaning implementation will be designed using component behaviours (competence modules) and are not learnt – a violation of point 4 above. We keep this in mind during the implementation and attempt to remain hyper-vigilant to minimise the impact of our anthropocentric interpretations of competence module labels on the ABBA network structure.

2.3.3 LEARNING Our lay understanding of the term ‘learning’ is necessarily anthropocentric. With the advent of scientific research into learning in disciplines like machine learning, many researchers have attempted to give precise definitions to the term. Our intuition may lead us to think ‘learning’ is a general abstraction for some identifiable properties of systems that learn. However, this is a consequence of reduction. Closer inspection reveals that it is a category encompassing a heterogeneous collection of empirically observed phenomena – for which there are no universal properties. Biological agents use a myriad of learning mechanisms each specialised for particular tasks. As Deacon puts it, “Learning is not any one general process. Learning always occurs in a particular context, involving particular senses and types of motor actions, and certain ways of organizing the information involved.” [Deacon, 1997] p48. We can adopt a narrower definition of leaning, as is often done, and develop algorithms that implement this type of learning well. Unfortunately, these general algorithms will often fair poorly in specific contexts involving embodied agents. Learning algorithms that utilise domain knowledge and tailored representations can usually outperform generalist solutions. This is exactly what evolution has created.

5. Learning is not any one general process In accordance with point 2 – ‘Most representations are learnt’, ABBA includes learning as an integral part of the action-selection substrate. However, we do not attempt to solve all learning problems – only the Pavlovian-like learning [Pavlov, 1926] of iconic associations (indexical references) is supported (section 4.4.1). Any ‘higher-level’ learning should be taskoriented and thus is not appropriate at the architecture level.

2.3.4 IMPLICATIONS FOR ROBOTICS Modern engineering has been very successful because design complexity has been tackled using functional modular decomposition. Hence, almost all engineered artifacts can be understood – reverse engineered – using purely reductionist techniques. An application software developer need not understand operating system design; an operating system programmer need not understand microprocessor design; and similarly a microprocessor designer need not know quantum mechanics or material science. As complex natural systems, such as animals, can be explained without the need for a designer, in this respect complexity is of no consequence. Complex biological systems are not

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amenable to the use of functional decomposition to understand their design – because they are not designed. Researchers in the biological, medical and other natural sciences are being forced to appreciate this, as the limits of reductionist understanding are now apparent. Decades ago medical science could get by with the assumption that our bodies were composed of largely independent sub-systems – the nervous system, circulatory system, limbic system, endocrine system, digestive system and skeletal-muscular system. Modern medicine is still trying to deal with increasing knowledge of complex dependencies between these ‘sub-systems’. In many ways, they are almost as tightly integrated with each other as within themselves. Although much has been learned using this arbitrary functional division of the human body, it has almost outlived its usefulness to provide new insight. Are we doomed to attempt understanding of such horrendously complex systems without the tools of functional decomposition – only holistically? Nature does face a complexity problem in one respect – “How can a complex organism be efficiently expressed using genetic code?”. Genes produce organisms. Genes do not specify an organism in the sense that a construction plan specifies a building. Genes do not code for behaviour, or neural wiring, or even a body plan. Genes specify a process – an extremely complex process, that in concert with environmental interaction, gives rise to higher-order emergent organisation – an organism. Much of the information required to create an organism is in the context. The solution nature employs uses some key properties of non-linear dynamical systems to advantage in order to deal with the complexity-of-expression problem. Researchers in the natural sciences, particularly biological science, are finding that these properties, such as emergence, are being employed ubiquitously in natural systems at many levels and across a broad range of contexts (eg. [Mitchell et al., 1994; Mitchell, 1997]). Luckily for us, complex non-linear dynamics, chaos theory and related disciplines are beginning to provide a new universal tool for understanding complex natural systems [Mitchell, 1998]. We believe that for significant further progress to be made in engineering complex artificial systems, these same tools must be additionally employed. For a more thorough treatment of ‘complexity’ and what it can mean, see [Gell-Mann, 1994]. As we have seen, Behaviour-based thinking has yielded some specific arguments to explain why the classical AI approach hasn’t produced the expected results – which have been much publicised for some time (eg. [Brooks, 1991; Brooks, 1991a]). Despite this, it is interesting to note that it didn’t capture the minds of the whole robotics community as many expected. Consequently, contemporary research is quite neatly divided into the classical and behaviour-based schools. Many behaviour-based researchers wonder why the classical approach persists. One reason, is a common misconception to equate behaviour-based systems with purely reactive systems having no state. In addition, Brooks, whose name is synonymous with behaviour-based robotics, published a paper titled “Intelligence without Representation” that is often misunderstood to mean no representation rather than no explicit representation, as Brooks himself points out [Brooks, 1991] pp19. However, a critical look at present systems developed using the behaviour-based approach shows that they fail to deliver what many applications require. In particular, most current behaviour-based systems are lacking in An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

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‘higher-level’ behaviour. Due to the way they are constructed they behave non-deterministically, like biological organisms. Although theoretically such systems can be considered predictable stochastically, many traditional classical researchers, in applications domains, see the non-determinism as unpredictable in comparison to the deterministic behaviour of classical systems. This is one reason why they are reluctant to embrace behaviour-based systems. The behaviour-based community may have to develop theoretical tools for providing bounds on system reliability before the systems can be accepted into some domains – such as medical and aerospace applications.

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2.4 SUMMARY This chapter has meet three main goals. Firstly, we have uncovered flaws in the implicit assumptions behind the classical AI derived sense-plan-act paradigm of robot control. Namely, the Cartesian dualism inherited from cognitive psychology and traditional science in general. Secondly, we investigated how this dualistic influence affected the classical notion of symbols and contrasted it with a biological understanding. In order to understand symbolic intelligence from the biological standpoint, we first set the context by examining representation – we saw that animals utilise iconic and indexical references. When then examined how the first symbol systems evolved in humans. Finally, we investigated the possible pitfalls that can result from this dualism and a flawed understanding of symbols. In particular, we discussed how anthropocentric representations lead to brittle systems and that learning is not one abstract process. We also argued that many behaviour-based systems still suffer from having anthropocentric symbol systems partially dictate their structure. This knowledge will be important in Chapter 4 when we discuss the design of the ABBA architecture. We will include specific mechanisms for iconic and indexical references as well as providing support for symbolic reference. As symbolic reference must be grounded to task and agent related affordances, we cannot include specific symbols at the architecture level.

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Chapter 3 C OOPERATION ´:LWKRXWDVRQJRUDGDQFHZKDWDUHZH"µ  ²¶7KDQN'HDFRQ@

F

rom the definition of symbols given in this thesis, we can say that humans have yet to build any artificial device capable of non-trivial symbolic understanding. The aim of Artificial Intelligence, Artificial Life and some Robotics research, is to do just this. While these fields are still in their infancy, we think the pace is beginning to quicken. The ability to build hierarchical symbolic representations seems to be a major ingredient missing from our attempts to synthesise intelligence from the bottom-up. ABBA provides all the sub-symbolic components, such as iconic and indexical reference that animals have profited from for so long. Artificial systems with the perceptual and motor sophistication of animals may be longer in coming. This is a mainly a technology problem. Whether symbol processing can substitute, to some extent, for poor perception remains to be seen. I suspect poor sensing may result in symbols with little power. However, I do agree with Deacon, that we will be engineering symbolic systems in the ‘not too distant future’18. In the mean time, the following section summaries what was achieved by our research and draws conclusions. Finally, we suggest how this work may be extended in the future.

7.1 CONCLUSIONS The main goal of this thesis was to develop an architecture suitable for supporting the distributed planning of cooperative behaviour in multi-robot systems. We have implemented a solution to the cooperative cleaning task we specified in terms of four layered solutions. The solutions scale the sophistication of cooperation and communication in the manner observed in the biological cooperative systems that we surveyed (section 3.3). The most sophisticated layer requires the ability to purposively navigate, cooperate and use symbolic communication. We developed a mechanism for navigation based on learning a spatial and topological representation of the laboratory. The representation arises from a combination of correlation learning, the provision of detectors for features that provide information about position – odometry and whisker or visual, and the action selection substrate. In accordance with point 2

18 It is important to remain ‘suitably vague’ when predicting the future.

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from Chapter 2 – ‘Most representations are learnt’, the map representation is not specified explicitly. The robot learns the spatial and topological adjacency of locations through behaving. The implementation of this mechanism demonstrated, for the first time, that situated, embodied agents without an explicitly specified map representation are capable of purposive navigation including spatial and topological path planning using a homogeneous action selection substrate. We also developed a mechanism for truly distributed planning of cooperative behaviour in behaviour-based agents. This is based on the rationale that, if the interaction between behaviour elements within an agent can accomplish action planning, then the interaction between behaviour elements among agents can accomplish cooperative action planning. Maes has shown that her spreading activation rules, which we adopted and extended for the ABBA action selection mechanism, are capable of action planning. In section 4.2.3 we illustrated that we can take an ABBA network, distribute it over multiple robots and it will still yield the same action sequences. The restriction is that all the conditions upon which the planning depends must be directly perceivable from the environment. In some cases, this may be true, but in many, the planning will depend on conditions internal to the agent’s collaborators – their intensions and other knowledge. In this situation, communication is necessary to convey the appropriate information. For this reason, we needed a mechanism to communication litter pile locations from Flo to Joh. Our examination of the classical AI based ‘sense-plan-act’ approach to robotics, revealed that it suffers from some well document problems, such as ungrounded anthropocentric symbols and consequently the frame problem. We determined that these result from the ubiquitous influence of Cartesian dualism. This led to the conclusion that, although one of the maxims of behaviour-based philosophy is analogous to ‘Most representations are learnt’ (point 2), it is still subtly influenced by the use of anthropocentric symbols during design. We argued that this problem can be avoided by recognising that ‘Most behaviour is learnt’ (point 4). The notion of a location as a position in a global coordinate space is an anthropocentric one. Hence, we could not communicate location using local odometry coordinates – as ‘Communicated signals have no intrinsic meaning’ (point 8). By our definition, a ‘location’ is a symbolic concept. Therefore, some mechanism for conveying the symbolic meaning was necessary. Looking to biology, we concluded that symbols are learnt to solve specific problems. Therefore, we devised a specific mechanism for learning a ‘location’ – the location labelling procedure that simultaneously grounds the concept in both robots (point 9). This demonstrated symbolic communication between behaviour-based agents – albeit in a narrow context involving a single symbol. The implementation of a concrete task – cleaning – required us to construct the two Yamabico robots and equip them with sensors and actuators suitable for cleaning. We designed bump sensors, a vacuum cleaner, fitted the robots with radio modems and invested time writing the RADNET communication software enabling inter-robot and robot-host communication to support our experiments. Our requirement for close wall following led to the development of a new type of sensor – a passive proportional whisker. The cooperation by observation and visual location labelling behaviour required the implementation of visual mechanisms for obstacle avoidance, tracking and litter pile location. The implementations of these used realConclusions

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time hardware-based template matching in a novel way. Some of the techniques used were adapted from other uses developed in our laboratory. Our quantitative proof shows the greater economy obtained by decomposing many problems into multi-robot systems. This was supported by our experimental results – which demonstrated the robustness and reliability gained by dividing the cleaning task over two robots. The layering of behaviour also afforded greater robustness to failure of selected behaviour mechanisms. The performance gains demonstrated as the layers utilised communication and awareness of other robots supports previous findings. We also show that distributed cooperative planning improves upon this for the cleaning task. Although our layered solutions aided in managing the complexity of design, the layers to not function as separate entities – as each builds on and requires the behaviour of those below it. The layers work together due to the homogeneous action selection mechanism. Hence, failures in specific components of lower layers may not render the layers above completely non-functional. The implementation of the cleaning task allowed us to compare the behaviour-based approach with a classical approach. We argue that although the cleaning task is straightforward enough to be implemented successfully using a classical or hybrid architecture, there are advantages to adopting a behaviour-based approach. Specifically, complexity is reduced since many unexpected situations are handled naturally – only resulting in lost performance. In addition, layering not only helps to manage implementation complexity, but also provides graceful degradation in the face of failure. A classical system would not exhibit this unless it was specifically designed – it is more likely that exceptional situations must be accounted for at design-time.

7.2 FUTURE DIRECTIONS As mentioned, we chose Maes’ action selection scheme due to the planning ability it provided. Although its winner-take-all nature did not prove to be a major problem for the implementation of the cleaning task, we believe will not scale to complex networks. It doesn’t allow independent behaviour to occur and ignores the considerations of loosing behaviour. Future research may be to devise an action selection mechanism capable of representing ‘plans’ in the dynamics of activation, but without the restrictions of winner-take-all. The final-common-path problem may be solved using ideas from the philosophy behind Rosenbatt’s utility fusion arbitration in DAMN ([Rosenblatt, 1997] pp55). In addition, in hindsight the smallest units of selectable action – the competence modules – were too coarse. This was in part dictated by the high-level interface to the Yamabico locomotion system. As mentioned, the consequent division of the possible action space of the robot into CMs violates point 4 – ‘Most behaviour is learnt’. A future action selection mechanism could be based around Steels’ process networks, for example [Steels, 1992]. The collections of processes that work together to realise a particular compound behaviour could then be learnt – and subsequently be activated as a unit. A learning mechanism suitable for this would need to be investigated. The spatial navigation mechanism involving the Kohonen self-organising map (SOM) was loosely based on the place cell neurones observed in rats [Muller et al., 1991; O’Keefe, 1991]. As we utilise location FDs spanning the floor space, this mechanism does not An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

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scale to larger maps well. Future research might investigate how a hierarchical representation may arise. New neurological research is revealing that the place-cell idea is an over simplification of the nature of these cells in the hippocampus of primates [Squire, 1992]. Perhaps neurological discoveries will soon provide new ideas about how space can be represented. The solution to the cleaning task did not involve extensive cooperative planning – the interactions were relatively simple. Most of the planning was for navigation. It would be interesting to implement a more complex task that would make greater use of the distributed cooperative planning capability of the networks. Future work could investigate this cooperative planning ability to address traditional cooperative robot issues, such as task decomposition and allocation. In addition, the communication dialogue was also relatively simple. Further research could attempt to use the planning capability to plan complex dialogues. Perhaps research into the development of language akin to the recent work of Steels could be incorporated (eg. [Steels, 1996a; Steels, 1996d]). On the cleaning front, we did not seriously investigate how cleaning might be performed efficiently, as this was not our aim. However, researchers interested in cleaning could investigate other ways to improve the performance. Most of the gains in performance from one layer to the next were due to shortening the path travelled by Joh in locating litter piles. The path could be further shortened by involving Flo. Rather than simply communicate litter piles, the robots could use the cooperative planning facility to implement a negotiation of a rendezvous location where litter will be dumped. This would be useful in environments requiring navigation between multiple interconnected rooms. Our implementation of perimeter following by Flo does not attempt to cover intelligently the boundaries of all furniture within a room. While Flo does often follow the boundary of furniture in the centre of a room – this only occurs because sometimes Flo looses tracking of the outer perimeter and happens to drive across open space areas. Further research may seek a mechanism to cover purposively all the boundaries within a multi-room environment. We think it may also be possible to use indexical references to implement mechanisms for keeping track of movable obstacles – so that the robots could push obstacles around or transport bins, for example. This would require indexes like ‘the-obstacle-I’m-pushing’ and ‘the-location-of-the-waste-paper-basket’. The top layer of the cleaning solution required a single symbol be learnt. It would be interesting to implement a more complex task that requires the learning of more symbols for its solution. We could call the symbol for a location that we utilised a ‘first-level symbol’ in the sense that is a direct association between an iconic and an indexical representation (as explained in section 5.3.3). As symbols can also be associations between other symbols, future research could attempt to construct a slightly more complex symbol system involving some ‘higher-level’ symbols. In general, we believe this is one direction behaviour-based research should go – the creation of ever more complex grounded symbol systems. While ‘going up’ also requires a broader base of behaviour and perception, we believe that simple symbol systems involving a handful of symbols may be possible now. Devising appropriate tasks such that such symbols arise as an agent learns to solve specific problems may be tricky.

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7.3 SUMMARY ´7KH ZRUOG LV IXOO RI MXGJPHQWGD\V DQG LQWR HYHU\ DVVHPEO\ WKDW D PDQ HQWHUVLQHYHU\DFWLRQKHDWWHPSWVKHLVJDXJHGDQGVWDPSHGµ  ²5DOSK:DOGR(PHUVRQ² 86HVVD\LVWSRHWSKLORVRSKHU  (VVD\V´6SLULWXDO/DZVµ)LUVW6HULHV 

This thesis contains a number of contributions to the robotics field. We hope that each reader has found some lessons that he or she will find useful in their particular research endeavour. In summary, the major contributions of this thesis are: • Demonstration of truly distributed cooperative planning in a heterogeneous behaviourbased multi-robot system. • Demonstration of a mechanism for spatial and topological map-learning and purposive navigation involving no explicit representations in a behaviour-based system. • Demonstration of a specific mechanism for learning and communicating a symbol for a location by first independently grounding it in each agent. • Illustration of the robustness and decreased complexity afforded by layered task implementations that work together in a homogeneous action selection environment. • Illustration of the performance gains afforded by utilising visual awareness of other agents and both state and symbolic communication. • Development of a unique proportional whisker utilised for wall following and detecting tactile features. • An argument that illustrates how anthropocentric symbol systems are subtly influencing the design of many behaviour-based systems that adhere to the embodiment and situatedness maxims. We proposed recognising that ‘Most behaviour is learnt’ might help. This work was intended to bring the robotics research community one step closer to an understanding of how intelligent symbolic agents and agent ‘societies’ can be constructed. We have shown that the beginnings of symbolic understanding, within and between situated agents, are within reach. However, as AC/DC put it “It’s long way to the top (if you wanna Rock 'n' Roll)”19.

19 Song title of an Australian Rock’n’Roll band formed in Sydney in 1973 [Done, 1995].

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B IBLIOGRAPHY [Albus et al., 1987] Albus, J.S., McCain, H.G., and Lumia, R., “NASA/NBS Stanford Reference Model for Telerobot Control System Architecture (NASREM)”, NIST Technical Note 1235, NIST, Gaithersburg, MD, July 1987. [Alter, 1992] Alter, T. D., “3D Pose from 3 Corresponding Points under Weak-Perspective Projection”, Massachusetts Institute of Technology, AI Laboratory, A.I. Memo No. 1378, July 1992. [Aristotle, 350BC] Aristotle, “Posterior Analytics”, written in Greek, 350BC. http://classics.mit.edu/Aristotle/posterior.html.

See also

[Arkin, 1989] Arkin, Ronald C., “Towards the Unification of Navigational Planning and Reactive Control”, AAAI Spring Symposium on Robot Navigation Working Notes, 1-5, March, 1989. [Arkin and Hobbs, 1992b] Arkin, R. C. and Hobbs, J. D., “Dimensions of Communication and Social Organization in Multi-Agent Robotic Systems”, Proc. Simulation of Adaptive Behavior 92, Honolulu, HI, Dec., 1992. [Axelrod, 1984] Axelrod, R., “The evolution of cooperation”, New York: Basic Books, 1984. [Balch and Arkin, 1994] Balch, Tucker and Arkin, Ronald C., “Communication in Reactive Multiagent Robotic Systems”, Autonomous Robots, 1, 27-52, Kluwer Academic Publishers, Boston, 1994. [Barnes and Gray, 1991] Barnes, D. and Gray, J., “Behaviour synthesis for co-operant mobile robot control”, In international conference proceedings on Control, pp1135-1140, 1991. [Bennett, 1988] Bennett, Stuart, “Real-Time Computer Control: An Introduction”, Prentice Hall International series in systems and control engineering. Ed. M. J. Grimble. 1988. [Blumberg, 1994]Blumberg, Bruce, “Action-Selection in Hamsterdam: Lessons from Ethology”, MIT Media-Laboratory, E15-305F, 20 Ames St., Cambridge Ma. 02139, 1994. [Boden et al., 1996] Boden, Margaret A., (ed.), “The Philosophy of Artificial Life”, Oxford Readings in Philosophy, Oxford University Press, ISBN 0-19-875154-0, 1996. [Bond, 1996] Bond, Alan H., “An Architectural Model of the Primate Brain”, Dept. of Computer Science, University of California, Los Angeles, CA 90024-1596, Jan 14, 1996. [Boznic, 1986]

Boznic, S. M., “Digital and Kalman Filtering”, Edward Arnold Publishers, 1986.

[Brady, 1984] Brady, Michael, “Artificial Intelligence and Robotics”, NATO ASI Series, Vol. F11, Robotics and Artificial Intelligence, Edited by M. Brady et el., Springer-Verlag Berlin Heidelberg. 1984.

Bibliography

155

[Brooks, 1986] Brooks, R. A., “A robust layered control system for a mobile robot”, IEEE Journal of Robotics and Automation., Volume 2, Number 1, 1986. [Brooks and Connell, 1987] Brooks, Rodney A. and Connell, Jonathan H., “Asynchronous Distributed Control System for a Mobile Robot”, SPIE’s Cambridge Symposium on Optical and Opto-Electronic Engineering Proceedings, Vol. 727, October, 1986, 77-84. [Brooks, 1987a] Brooks, Rodney A., “A Hardware Retargetable Distributed Layered Architecture for Mobile Robot Control”, MIT Artificial Intelligence Lab, Cambridge, Mass, 1987. [Brooks, 1990] Brooks, Rodney A., “Elephants Don’t Play Chess”, Robotics and Autonomous Systems 6, pp3-15, 1990. [Brooks, 1991] Brooks, Rodney A., “Intelligence Without Reason”, MIT AI Lab Memo 1293, April 1991. Prepared for Computers and Thought, IJCAI-91. [Brooks, 1991a] Brooks, Rodney A., “Intelligence Without Representation”, Artificial Intelligence, 47, pp 139-160, 1991. [Burgess et al., 1995] Burgess, Neil, Michael, Recce and O’Keefe, John, “Hippocampus: Spatial Models”, The Handbook of Brain Theory and Neural Networks, Michael A. Arbib (ed.), MIT Press, Bradford Books, ISBN 0-262-01148-4, pp468, 1995. [Byrne, 1995] Byrne, R., “The thinking ape, evolutionary origins of intelligence”, Oxford University Press, 1995. [Calvin, 1996] Calvin, William H., “How Brains Think: Evolving intelligence, Then & Now”, Phoenix publication, ISBN 0 75380 200 7, 1996. [Cao et al., 1995] Cao, Y. Uny, Fukunaga, Alex S., Kahng, Andrew B. and Meng, Frank, “Cooperative Mobile Robotics: Antecedents and Directions”, IEEE 0-8186-7108-4/95, 1995. [Capra, 1991] Capra, Fritjof, “The Tao of Physics : An Exploration of the Parallels Between Modern Physics and Eastern Mysticism”, Published by Shambhala Publications, ISBN 0877735948, 1991. [Cattoni et al., 1995] Cattoni, Roldano, Di Caro, Gianni, Aste, Marco and Caprile, Bruno, “Bridging the Gap between Planning and Reactivity: a Layered Architecture for Autonomous Indoor Navigation”, IRST - Istituto per la Ricerca Scientifica e Tecnologica, Povo, I-38050 Trento, Italy. [Cheney and Seyfarth, 1990] Cheney, D. L., Seyfarth, R. M., “How Monkeys see the world”, University of Chicago Press, 1990. [Cheng and Zelinsky, 1996] Cheng, Gordon and Zelinsky, Alexander, “Real-Time Visual Behaviours for Navigating a mobile Robot”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol 2. pp973. November 1996. [Churchland et al., 1992] Churchland, P. S. and T. Sejnowski, “The computational brain”, Cambridge, MA, MIT Press, 1992. [The Columbia Dictionary of Quotations] University Press, 1993, 1995.

The Columbia Dictionary of Quotations, Columbia

An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

156

[Connah and Wavish, 1990] Connah, David and Wavish, Peter, “An experiment in Cooperation”, Decentralized A.I., Yves Demazeau & Jean-Pierre Müller (Eds.), Elsevier Science Publishers B.V. (North Holland), 1990. [Cornman et al., 1982] Cornman, James W., Lehrer, Keith and Pappas, George S., “Philosophical rd Problems and Arguments: An Introduction”, 3 Ed., Hackett Publishing Company Inc., Indiana, ISBN 0-87220-044-2, 1982. [Crespi and Choe, 1997] Crespi, B. J. and Choe, J. C. (eds.), “The Evolution of Social Behaviour in Insects and Arachnids”, Cambridge, Cambridge University Press, 1997. [Croy and Hughes, 1991] Croy, M. and Hughes, R., “Effects of Food Supply, Hunger, Danger, and Competition on choice of Foraging Location by the fifteen-spined stickleback”, Animal Behaviour, Vol. 42, 1991. [Crutchfield and Young, 1990] Crutchfield, J. P. and Young, K., “Computation at the Onset of Chaos”, in “Entropy, Complexity, and Physics of Information”, W. Zurek, editor, SFI Studies in the Sciences of Complexity, VIII, Addison-Wesley, Reading, Massachusetts, pp 223-269, 1990. [Damasio, 1994] Damasio, A. R., “Descartes’ Error”, Grosset/Putnam, NY., 1994. [Dautenhahn, 1997] Dautenhahn, K., “Social modelling - on selfish genes, social robots and the role of the individual”, submitted to: Int Journal in Computer Simulation, Special issue on the Simulation of Social Behavior, 1997. [Dawkins, 1996] Dawkins, Richard, “Climbing Mount Improbable”, Penguin Books Ltd., London, ISBN 0 14 02.6302 0, 1996. [Deacon, 1997] Deacon, Terrance, “The Symbolic Species: The co-evolution of language and the human brain”, Penguin Books, ISBN 0-713-99188-7, 1997. [Dennett, 1993] Dennett, Daniel C., “Consciousness Explained”, Penguin Books, ISBN 0-14-01.28670, 1993. [Descartes, 1637] Descartes, R., “Discourse on Method and the Meditations”, 1637, Translated by John Veitch. Prometheus Books, 1989. [Diamond, 1991] Diamond, Jared, “The rise and fall of the third chimpanzee”, Vintage Books, ISBN 009 991380 1, 1991. [Done, 1995] Done, Malcolm (ed.), “AC/DC : The Kerrang! Files! : The Definitive History”, Virgin Pub Ltd., ISBN: 0863699081, December 1995. [Edelman, 1994] Edelman, S., “Biological Constraints and the Representation of Structure”, in Vision and Language, Psycoloquy 5 (57), 1994. [Edelman, 1998] Edelman, S., “Representation and Recognition in Vision”, MIT Press, (forthcoming), 1998. [Everett, 1988] Everett, H. R., “Survey of Collision Aviodance and Ranging Sensors for Mobile Robots”, Tech. Report No. 1194, Naval Command Control and Ocean Surveillance Center, San Diego, CA, 29 March, 1988. [Fearing, 1990] Fearing, R. S., “Tactile Sensing Mechanisms”, The International Journal of Robotics Research, Vol. 9, No. 3, pp 3-23, June 1990.

Bibliography

157

[Fodor and Pylyshyn, 1981] Fodor, J. A. and Pylyshyn, Z. W., “How Direct is Visual Perception? Some Reflections on Gibson's ‘Ecological Approach’”, Cognition, 9, pp139-196, 1981. [Ford and Hayes, 1991] Ford, K. and Hayes, P. (eds), “Reasoning Agents in a Dynamic World: The Frame Problem”, JAI Press, 1991. [Gatherer, 1997] Gatherer, Derek, “Macromemetics: Towards a Framework for the Re-unification of Philosophy”, Journal of Memetics - Evolutionary Models of Information Transmission, 1, 1997, http://www.cpm.mmu.ac.uk/jom-emit/1997/vol1/gatherer_dg.html. [Gaussier and Zrehen, 1994] Gaussier, Philippe and Zrehen, S., “The probabilistic topological map (ptm): A self-organizing and fast learning neural map that preserves topology”, Neural Networks, 1994. [Gaussier et al., 1998] Gaussier, Philippe, Joulain, C. and Banquet, J. P., “Motivated Animat navigation: a visually guided approach”, to in appear in proceedings Simulation of Adaptive Behaviour (SAB), Zurich, 1998. [Gell-Mann, 1994] Gell-Mann, Murray, “The Quark and the Jaguar: Adventures in the Simple and the Complex”, An Abacus Book, London, ISBN 0 349 10649 5, 1994. [Gill, 1997] Gill, Jerry H., “If a Chimpanzee Could Talk and Other Reflections on Language Acquisition”, The University of Arizona Press, ISBN 0-8165-1669-3, 1997. [Goodwin, 1981] Goodwin, Charles, “Conversational Organization : interaction between speakers and hearers”, Academic Press, New York and London, 1981. [Grewal and Andrews, 1993] Grewal, M.S. and Andrews, A.P., “Kalman Filtering Theory and Practice”, Prentice-Hall, Englewood Cliffs, New Jersey, 1993. [Grimsin et al., 1992] Grimsin, W. E. L., Huttenlocher, D. P., and Alter, T. D., “Recognizing 3D Objects from 2D Images: An Error Analysis”, in Proc. IEEE Conf. Computer Vision Pat. Rec., 1992. [Halliday, 1994] Halliday, T. R., “Sex and evolution”, in Halliday, T. R. and Slater, P. J. B. (eds.), “Behaviour and Evolution”, Cambridge University Press, ISBN 0-521-41858-5, pp150, 1994. [Hamilton, 1964] Hamilton, W. D., “The genetical evolution of social behaviour”, Journal of Theoretical Biology, 7, pp1-16 & 17-52, 1964. [Haralick and and Shapiro, 1993] Haralick, Robert M., and Shapiro, Linda G., “Computer and Robot Vision”, Vol. 2, Addison-Wesley Publishing Co., ISBN 0-201-56943-4 (v. 2), 1993. [Hardcastle, 1992] Hardcastle, V. G., “Reduction, explanatory extension, and the mind/brain sciences”, Philosophy of Science 59, pp408-28, 1992. [Heinzmann and Zelinsky, 1997] Heinzmann, J. and Zelinsky, A., “Robust Real-Time Face Tracking and Gesture Recognition”, Proceedings of IJCAI’97, International Joint Conference on Artificial Intelligence, August 1997. [Horswill, 1994] Horswill, I., “Visual Collision Avoidance by Segmentation”, Proc. Intelligent Robots and Systems (IROS), pp. 902-909, 1994.

An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

158

[American Heritage Dictionary, 1992] Houghton Mifflin Company, Ltd., “The American Heritage® Dictionary of the English Language”, Third Edition copyright © 1992 by Houghton Mifflin Company. Electronic version licensed to Microsoft Corporation from INSO Corporation. [Inaba, 1997] Inaba, M., “Remote-Brained Robots”, Proceedings of Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp 1593-1606, 1997. [Inoue, 1985] Inoue, H. et. al., “A Flexible Multi-Window Vision System for Robots”, Proceeding nd of 2 International Symposium on Robotics Research, MIT Press, 1985. [Jackson, 1994] Jackson, E. Atlee, “The Second Metamorphosis of Science: A Working Paper”, Technical Report CCSR-94-1, Center for Complex Systems Research, The Beckman Institute, University of Illinois at Urbana-Champaign, Feburary 1994. [Jin et al., 1994] Jin, K., Liang, P. and Beni ,G., “Stability of synchronized control of discrete swarm structures”, Proc. IEEE International Conference on Robotics and Automation (ICRA), pp10331038, 1994. [Jung et al., 1995] Jung, David, Stanley, Ben, Zelinsky, Alex, McKerrow, Phillip, Ohya, Akihisa, Tsubouchi, Takashi and Yuta, Shin’ichi, “Collaborative Research in Intelligent Autonomous Systems between the Universities of Wollongong and Tsukuba”, Proceedings of the Australian Robot Association (ARA) conference, Melbourne, July, 1995. pp222-229. [Jung, 1995b] Jung, David, “Cooperation between Autonomous Agents”, Proceedings 1995 Yamabico Symposium. Intelligent Robotics Laboratory technical report, University of Tsukuba, Tsukuba Japan, 1995. [Jung and Zelinsky, 1996] Jung, David and Zelinsky, Alexander, “Cleaning: An Experiment in Autonomous Robot Cooperation”, Proceedings of the World Automation Congress (WAC) International Symposium on Robotics and Manufacturing (ISRAM), vol 3. pp351-356, Montipellier, France, March, 1996. [Jung and Zelinsky, 1996a] Jung, David and Zelinsky, Alexander, “Whisker-Based Mobile Robot Navigation”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol 2. pp497-504, November 1996. [Jung and Stanley, 1996b] Jung, David and Stanley, Ben, “Yamabico Autonomous Mobile Robots”, Intelligent Robotics Laboratory technical report, University of Wollongong, Wollongong, Australia, 1996. [Jung et al., 1997] Jung, David, Cheng, Gordon and Zelinsky, Alexander, “An Experiment in Realising Cooperation between Autonomous Mobile Robots”, Fifth International Symposium on Experimental Robotics (ISER), pp513-524, Barcelona, Catelonia, June 1997. [Jung et al., 1998a] Jung, David, Heinzmann, Jochen and Zelinsky, Alexander, “Range and Pose Estimation for Visual Servoing on a Mobile Robotic Target”, Proc. IEEE International Conference on Robotics and Automation (ICRA), vol 2. pp1226-1231, Leuven, Belgium, May 1998. [Jung et al., 1998b] Jung, David, Cheng, Gordon and Zelinsky, Alexander, “Robot Cleaning: An Application of Distributed Planning and Real-time Vision”, Field and Service Robotics, Springer, Alexander Zelinsky (ed.), pp187-194, 1998.

Bibliography

159

[Jung and Zelinsky, 1998c] Jung, David and Zelinsky, Alexander, “An Architecture for Distributed Cooperative-Planning in a Behaviour-based Multi-robot System”, Journal of Robotics & Autonomous Systems (RA&S), special issue on Field & Service Robotics, 1998. [Kaiser and Losick, 1993] Kaiser, D. and Losick, R., “How and Why Bacteria Talk to Each Other”, in Cell, Vol. 73, No. 5, pp873-885, June 4, 1993. [Kalman, 1961] Kalman, R.E., “New Methods and Results in Linear Filtering and Prediction Theory”, ASME J. Basic Engineering, Series D, Vol. 83, 1961. [Kanade et al., 1983] Kanade, T., and Kender J. R., “Mapping Image Properties into Shape Constraints: Skew Symmetry, Affine-Transformable Patterns, and the Shape-from-Texture Paradigm”, Human and Machine Vision, Beck, Hope, and Rosenfeld, Eds., Academic Press, 1983. th

[Kanayama, 1983] Kanayama, Y., “concurrent programming of intelligent robots”, In 8 IJCAI, Karlsruhe (FRG), pp834-838, 1983.

[Kapur et al., 1985] Kapur, D., et al., “Reasoning about Three Dimensional Space”, Proceedings of the International Conference on Robotics and Automation, St. Louis, MO, 1985, pp. 405-410. [Kohonen, 1990] Kohonen, Teuvo, “The self-organising map”, Proceedings of IEEE, 78(9):1464-1479, Sep. 1990. [David M. Kreps and John Roberts, 1982] David M. Kreps, Paul Milgrom, John Roberts, and Robert Wilson, “Rational Cooperation in the Finitely Repeated Prisoners’ Dilemma”, Journal of Economic Theory 27, pp 245-52, 1982. [Kube and Zhang, 1994] Kube, C. Ronald, Zhang, Hong, “Collective Robotics: From Social Insects to Robots”, Adaptive Behaviour, Vol. 2, No. 2, 189-218. 1994. [Kuniyoshi et al., 1994] Kuniyoshi, Yasuo, Rougeaux, Sebastien, Ishii, Makoto, Kita, Nobuyuki, Sakane, Shigeyuki and Kakikura, Masayoshi, “Cooperation by Observation [The framework and basic task patterns]”, Proceedings of the 1994 IEEE International Conference on Robotics and Automation, San Diego, California, May 8-13, 1994. [Lamarck, 1809] Lamarck, Jean Baptiste, “Philosophie Zoologique” (Zoological Philosophy), 1809, reprinted edition Lubrecht & Cramer Ltd., ISBN 3768200280, December 1960. [Latombe, 1991] Latombe, J-C, “Robot Motion Planning”, Kluwer Academic Publishers, 1991. [Leckie-Tarry, 1995] Leckie-Tarry, Helen, “Language & Context: A functional linguistic theory of register”, Pinter, London & NewYork, ISBN 1 85567 271 5, 1995. [Lee, 1996] Lee, David, “The Map-Building and Exploration Strategies of a Simple SonarEquipped Mobile Robot”, Distinguished Dissertations in Computer Science, Cambridge University Press, ISBN 0 521 57331 9, 1996. [Leonard and Durrant-Whyte, 1991] Leonard, J. J. and Durrant-Whyte, H. F., “Simultaneous map building and localization for an autonomous mobile robot”, in Proc. IEEE Int. Workshop on Intelligent Robots and Systems, Osaka, Japan, November, 1991. [Lorigo, 1997] Lorigo, Liana M., “Visually-Guided Obstacle Avoidance in Unstructured Environments”, Proceedings Intelligent Robots and Systems (IROS’97), v2, pp 373-379, Grenoble, France, September 7-11, 1997.

An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

160

[Ludlow, 1980] Ludlow, A., “The evolution and simulation of a decision maker”, in Toates, F. and Halliday, T., “Analysis of Motivational Processes”, Academic Press, London, 1980. [Maes, 1990a] Maes, P., “Situated Agents Can Have Goals.”, Designing Autonomous Agents. Ed: P. Maes. MIT-Bradford Press, 1991. ISBN 0-262-63135-0. Also published as a special issue of the Journal for Robotics and Autonomous Systems,Vol. 6, No 1, North-Holland, June 1990. [Maes and Brooks, 1990b] Maes, P. and Brooks, R. A., “Learning to Coordinate Behaviors”, proceedings AAAI, American Association for Artificial Intelligence, 1990. [Mallot, 1995] Mallot, Hanspeter A., “Layered Computation in Neural Networks”, The Handbook of Brain Theory and Neural Networks, Michael A. Arbib (ed.), MIT Press, Bradford Books, ISBN 0262-01148-4, pp513, 1995. [Mandik, 1997] Mandik, Pete, entry on Emergence in Eliasmith, Chris (ed.), “Dictionary of Philosophy of Mind”, web publication of the Department of Philosophy, Washington University, St. Louis, MO, USA, http://artsci.wustl.edu/~philos/MindDict/, 1997. [Matari•, 1992] Matariþ, Maja J., “Integration of Representation Into Goal-Driven Behavior-Based Robots”, in IEEE Transactions on Robotics and Automation, Vol. 8, No. 3, June 1992, 304-312. [Matari•, 1992a] Matariþ, Maja J., “Behavior-Based Systems: Key Properties and Implications” in Proceedings, IEEE International Conference on Robotics and Automation, Workshop on Architectures for Intelligent Control Systems, Nice, France - May 1992, 46-54. [Matari•, 1992b] Matariþ, Maja J., “Minimizing Complexity in Controlling a mobile Robot Population”, Proceedings of the 1992 IEEE International Conference on Robotics and Automation, Nice, France - May 1992. [Matari•, 1994a] Matariþ, Maja J, “Interaction and Intelligent Behavior”, MIT EECS PhD Thesis, May 1994, MIT AI Lab Tech Report AITR-1495, August 1994. [Matari•, 1995] Matariþ, Maja J., “Issues and Approaches in the Design of Collective Autonomous Agents”, to appear in “Moving the Frontiers Between Robotics and Biology”, special issue of Robotics and Autonomous Systems, Vol. 16, Nos. 2-4, December 1995. [Maybeck, 1979] Maybeck, P., “Stochastic Models, Estimation and Control”, Vol 1, Academic Press, 1979. [Maynard Smith, 1978] Maynard Smith, J., “The Evolution of Sex”, Cambridge, Cambridge University Press, 1978. [McCarthy and Hayes, 1969] McCarthy, John and Hayes, Pat, “Philosophical Problems from the Standpoint of Artificial Intelligence”, Machine Intelligence 4, 1969. [McFarland and Bösser, 1993] McFarland, David and Bösser, Thomas, “Intelligent Behavior in Animals and Robots”, The MIT Press, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, ISBN 0-262-13293-1. [McLauchlan et al., 1994] McLauchlan, P.F., Reid, I.D., Murray, D.W., “Recursive Affine Structure and Motion from Image Sequences”, Lecture Notes in Computer Science Vol.800, Proceedings of ECCV’94, pp 217-224, ISBN 3-540-57956-7, Springer Verlag Berlin Heidelberg 1994. [McLaughlin, 1992] McLaughlin, Brain P, “The rise and fall of British Emergentism”, in Beckermann, Ansgar, Flohr, Hans and Kim, Jaegwon (eds.), “Emergence or Reduction? : Essays Bibliography

161

on the Prospects of Nonreductive Physicalism”, Walter De Gruyter; ISBN 3110128802, September 1992. [Mitchell et al., 1994] Mitchell, Melanie, Crutchfield, James P., and Hraber, Peter, “Dynamics, Computation, and the ‘Edge of Chaos’: A Re-Examination”, In “Complexity: Metaphors, Models, and Reality”, edited by G. Cowan, D. Pines, and D. Meltzer. SFI Studies in the Sciences of Complexity, Proc. Vol. XIX, 497-513. Reading, MA: Addison-Wesley, 1994. [Mitchell, 1997] Mitchell, Melanie, “Theories of Structure versus Theories of Change (Commentary on T. van Gelder, ‘The Dynamical Hypothesis in Cognitive Science’)”, Submitted to Behavioral and Brain Sciences, also WWW publication: http://www.santafe.edu/~mm/, 1997. [Mitchell, 1998] Mitchell, Melanie, “Complexity and the Future of Science”, Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, WWW publication: http://www.santafe.edu/~mm/, 1998. [Moravcsik, 1992]

Moravcsik, J., “Plato and Platonism”, Cambridge, MA: Blackwell, 1992.

[Moravec, 1980] Moravec, Hans P., “Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover”, Ph.D. thesis, Stanford University, Stanford, California, May 1980. Available as Stanford AIM-340, CS-80-813 and CMU-RI-TR-3. [Muller et al., 1991] Muller, R. U., Kubie, J. L., Bostock, E. M., Taube, J. S. and Quirk, G. J., “Spatial firing correlates of neurons in the hippocampal formation of freely moving rats”, Brain and Space, Paillard (Ed.), Oxford: Oxford University Press, pp 296-333, 1991. [Neibur, 1995] Neibur, Dagmar, “An Example Of Unsupervised Networks Kohonen’s Self Organizing Feature Map”, NASA Jet Propulsion Laboratory (JPL) technical report 826, California Institute of Technology, PaswJenajCA91109, http://techreports.jpl.nasa.gov/1995/1995abs.htm, 1995. [Newell and Simon, 1976] Newell, A. and Simon, H.A., “Computer science as empirical enquiry: symbols and search”, Communications of the Association for Computing Machinery, 19, 113-126, 1976. [Nicholls and Lee, 1989] Nicholls, Howard R. and Lee, Mark H., “A Survey of Robot Tactile Sensing Technology”, The International Journal of Robotics Research, Vol. 8, No 3, pp. 3-29, June 1989. [O’Keefe, 1991] O’Keefe, J., “The hippocampal cognitive map and navigational strategies”, Brain and Space, Paillard (Ed.), Oxford: Oxford University Press, pp 273-295, 1991. [Parker, 1994b] Parker, Lynne E., “ALLIANCE: An Architecture for Fault Tolerant Multi-Robot Cooperation”, submitted to IEEE Transactions on Robotics and Automation, December 1994 (available as ORNL/TM-12920, February 1995) [Parker, 1995] Parker, Lynne E., “The Effect of Action Recognition and Robot Awareness in Cooperative Robotic Teams”, IEEE 0-8186-7108-4/95, 1995. [Pasteels et al., 1987] Pasteels, J. M., Deneubourg, J. and Goss, S., “Self-organization mechanisms in ant societies: Trail recruitement to newly discovered food sources”, in Pasteels, J. M. and Deneubourg, J. (eds.), “From individual to collective behavior in social insects”, Basel: Birkäuser Verlag, 1987. [Pavlov, 1926]

Pavlov, Ivan Petrovich, “Conditioned reflexes”, Oxford University Press, 1926.

An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

162

[Pfeifer, 1995] Pfeifer, Rolf, “Cognition - Perspectives from autonomous agents”, Robotics and Autonomous Systems 15, pp47-70, 1995. [Pylyshym, 1987] Pylyshym, Z. W. (ed.), “The Robot’s Dilemma. The Frame Problem in Artifical Intelligence”, 1987. [Pöppel and Schill, 1995] Pöppel, Ernst and Schill, Kerstin, “Time Perception: Problems for Representation and Processing”, The Handbook of Brain Theory and Neural Networks, Michael A. Arbib (ed.), MIT Press, Bradford Books, ISBN 0-262-01148-4, pp987-990, 1995. [Rapoport et al., 1976] Rapoport, A., Guyer, M.J., and Gordon, D.G., “The 2×2 Game”, Ann Arbor, MI: University of Michigan Press, 1976. [Rencken, 1993] Rencken, W. D., “Concurrent Localisation and Map Building for Mobile Robots Using Ultrasonic Sensors”, Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’93), v3. pp2192, July 1993. [Rhodes, 1995] Rhodes, Bradley, “Pronomes in Behaviour Nets”, Tech. Report #95-01, MIT Media Lab, Learning and Common Sense Section. Jan 1995. [Riekki and Kuniyoshi, 1996] Riekki, J. and Kuniyoshi, Y., “Behavior Cooperation Based on Markers and Weighted Signals”, Proceedings of the World Automation Congress (WAC) International Symposium on Robotics and Manufacturing (ISRAM), Montipellier, France, March, 1996. [Rosenblatt, 1997] Rosenblatt, Julio K., “DAMN: A Distributed Architecture for Mobile Navigation”, PhD thesis, The Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, CMU-RI-TR-97-01, 1997. [Russell, 1985] Russell, R. A., “Object Recognition Using Articulated Whisker Probes”, th Proceedings of the 15 ISIR, Tokyo, pp. 605-11, 1985. [Russell, 1990]

Russell, Andrew R., “Robot Tactile Sensing”, Prentice Hall, ISBN 0 13 781592 1.

[Smith et al., 1990] Smith, R., Self, M. and Cheesman P., “Estimating uncertain spatial relationships in robotics”, in I. Cox and G. Wilfong (eds.), Autonomous Robot Vehicles, Springer Verlag, 1990. [Squire, 1992] Squire, L. R., “Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans”, Psychological Review 3, pp172-179, 1992. [Srinivasan et al., 1995] Srinivasan, M.V., Chahl, J.S., Nagle, M.G., Weber, K., Venkatesh, S. and Zhang, S.W., “Low-level vision in insects, and applications to robot navigation”, In: Computational Intelligence: A Dynamic System Perspective, M. Palaniswami, Y. Attikouzel, R.J. Marks, D. Fogel, and T. Fukuda (eds), IEEE Press, Piscataway, N.J. pp. 312-326, 1995. [Srinivasan et al., 1997] Srinivasan, M.V., Chahl, J.S., and Zhang, S.W., , “Robot navigation by visual dead-reckoning: inspiration from insects”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 11, No. 1, pp 35-47, 1997. [Stains, 1984] Stains, H. J., “Carnivores”, in Anderson, S. and Jones, J. K. Jr. (eds.), “Orders and Families of Recent Mammals of the World”, John Wiley and Sons, N.Y., pp491-521, 1984. [Steels, 1990] Steels, Luc, “Cooperation between Distributed Agents through Self-Organisation”, VUB AI Lab, Pleinlaan 2, 1050 Brussels Belgium, IEEE IROS 90. Bibliography

163

[Steels, 1992] Steels, L., “The PDL reference manual”, memorandum No. 92-5, Brussels: VUB AI Lab. 1992. [Steels and Brooks, 1995] Steels, Luc and Brooks, Rodney (eds.), “The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents”, Lawrence Erlbaum Associates, Publishers, Hove UK. ISBN-0-8058-1518-X, 1995. [Steels, 1996] Steels, Luc, “The origins of intelligence”, Proceedings of the Carlo Erba Foundation, Meeting on Artificial Life. Fondazione Carlo Erba. Milano, March 12, 1996. [Steels, 1996a] Steels, Luc, “Synthesising the origins of language and meaning using co-evolution, self-organisation and level formation”, Artificial Intelligence Laboratory, Vrije Universiteit Brussel. In: Hurford, J. (ed.) Evolution of Human Language, Edinburgh University Press. Edinburgh, July 1996. [Steels, 1996d] Steels, Luc, “Perceptually grounded meaning creation”, In: Tokoro, M. (ed.) Proceedings of ICMAS-96, Kyoto 1996. Calif, AAAI Press. [Stein, 1997] Stein, L. A., “PostModular Systems: Architectural Principles for Cognitive Robotics”, Cybernetics and Systems, 28(6), pp471-487, September 1997. [Sycara, 1994] Sycara, Katia P., “A Computational Model of Negotiation in Concurrent Engineering”, Concurrent Engineering: Research and Applications, 1994 Conference. The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213. [Tarnas, 1991] Tarnas, Richard, “The Passion of the Western Mind”, Pimlico, London, ISBN 0-71267332-6, 1991. [Tennenholtz and Shoham, 1992] Tennenholtz, Moshe and Shoham, Yoav, “Emergent Conventions in Multi-Agent Systems”, Robotics Laboratory, Department of Computer Science, Stanford University, CA 94305. Proceedings of KR, Boston, 1992. [Thomson, 1993] Thomson, Keith Steward, “The common but less frequent loon and other essays”, Yale University Press, New Haven and London, ISBN 0-300-05630-3, 1993. [Torok, 1998] Torok, Simon (ed.), “The Helix”, CSIRO Double Helix science magazine, PO Box 225, Dickson, ACT, Australia 2602, Feb/March 1998. [Townsend and Busemeyer, 1995] Townsend, James T. and Busemeyer, Jerome, “Dynamic Representation of Decision-Making”, in Port, Robert F. and van Gelder, Timothy (eds.), “Mind as Motion: explorations in the dynamics of cognition”, A Bradford book, ISBN 0-262-16150-8, pp101, 1995. [Trivers, 1971] Trivers, R. L., “The evolution of reciprocal altruism”, Quarterly review of biology, 46, pp35-57, 1971. [Tylor, 1958] Tylor, Edward Burnett, “Primitive Culture Volume 11: Religion in Primitive Culture”, New York: Harper Torchbook, 1958 (1871). [Tyrrell, 1993] Tyrrell, Toby, “Computational Mechanisms for Action Selection”, PhD Thesis, University of Edinburgh, 1993. [Van Gelder, 1995] Van Gelder, T., “What might cognition be, if not computation?”, Journal of Philosophy, 91, 345-381, 1995.

An Architecture for Cooperation among Autonomous Agents. PhD Thesis – David Jung

164

[Wheeler, 1996] Wheeler, Michael, “From Robots to Rothko: The Bringing Forth of Worlds”, in Boden, Margaret A., (ed.), “The Philosophy of Artificial Life”, Oxford Readings in Philosophy, Oxford University Press, ISBN 0-19-875154-0, pp 209-236, 1996. [Wilson, 1971] Wilson, E. O., “The Insect Societies: Their Origin and Evolution”, New York: Narcourt, Brace & Co., 1971. [Wilson, 1975]

Wilson, E. O., “Sociobiology: The New Synthesis”, Harvard, 1975.

[Wright, 1995] Wright, Robert, “The Moral Animal : Why We Are the Way We Are : The New Science of Evolutionary Psychology”, Vintage Books, Luann Walther (Ed.), ISBN:0679763996, 1995. [Yuta and Premvuti, 1990] Yuta, Shin'ichi and Premvuti, Suparerk, “Consideration on the Cooperation of Multiple Autonomous Mobile Robots”, IEEE International Workshop on Intelligent Robots and Systems (IROS '90). [Yuta et al., 1991] Yuta, S., Suzuki, S. and Iida, S., “Implementation of a small size experimental self-contained autonomous robot - sensors, vehicle control, and description of sensor based behavior”, Proc. Experimental Robotics, Tolouse, France, LAAS/CNRS (1991). [Zelinsky et al., 1993a] Zelinsky, A., Jarvis, R.A., Byrne, J. and Yuta, S., “Planning Paths of Complete Coverage of an unstructured Environment by a Mobile Robot”, International Conference on Advanced Robotics (ICAR), pp533-538, Tokyo, Japan, November 1993. [Zelinsky et al., 1994] Zelinsky, A., Kuniyoshi, Y. and Tsukune H., “Monitoring and Coordinating Behaviours for Purposive Robot Navigation”, Proceedings of Intelligent Robotic Systems Conference (IROS), Munich, Germany, Sept. 1994.

Bibliography

165

G LOSSARY ABBA

Architecture for Behaviour Based Agents.

Activation

A property of ABBA competence modules that determines which is selected to execute at any given time.

Anthropomorphism

The attribution of human characteristics to non-human objects.

Anthropocentric

Interpreting reality exclusively in terms of human values and experience. The human symbol system is grounded in an anthropocentric categorisation of reality in terms of our affordances.

Behaviour

1) A functional connection between sensors and actuators (possibly complex). In robots, it is usually a programmatic connection, also called a schema. Behaviours are implemented in ABBA by CMs. 2) The observed or expressed actions of a robot or organism (in total).

Cartesian divide

A perspective on mind based around an “explanatory divide between mind and world, according to which fundamentally ‘internal’ subject relates to an ‘external’ world of objects via some interface.” ([Boden et al., 1996] p209). An indirect result of dualistic thinking and mindbody dualism.

Cartesian theatre

The part of the mind assumed to be the place where experience ‘all comes together’ into a conscious awareness. This seems to require an internal observer to view the play unfolding in what Dennett calls the Cartesian Theatre. The necessity of a Cartesian theatre comes about due to the Cartesian divide.

CM

Competence Module.

Competence Module

Smallest unit of selectable behaviour in ABBA networks (from Maes’ spreading activation networks [Maes, 1990a]).

Component Behaviour Behaviour (1), CM. ( Deictic reference (pronounced di k ′ti k ) point out or specify, as in the demonstrative pronoun this [American Heritage Dictionary, 1992]. As such, it is a type of flexible indexical reference – one in which what is referred to can change. Implemented in ABBA via Proxy FDs. Dualism

Theory that postulates that in addition to the physical world there is another ‘kind of stuff’ – such as mind as opposed to matter.

FD

Feature Detector.

Feature Detector

ABBA network unit that delivers a condition based on the external (sensory) or internal situation. Includes a confidence value [0…100].

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Iconic reference

Representation by similarity to stimulus. As any sensory system can only apprehend a limited set of characteristics of what it is sensing, the sensory representation is necessarily ‘iconic’. Most iconic references further limit the representation to some set of salient characteristics.

Indexical reference

Representation of associations between icons.

Litter Pile

1) Pile of small pieces of polystyrene foam on the floor, used to simulate litter (anthropocentric meaning). 2) Anything identified by the ‘interest’ operator – anything not matching carpet templates, surrounded by matches with carpet templates in the video frame (Joh’s meaning).

Meme

The smallest reproducible, transmittable unit of cultural, or memetic, evolution. An idea or concept. Analogous to a gene. Coined by Dawkins [Dawkins, 1996].

Panpsychism

The doctrine that all objects in the world have an inner being.

Proxy FD

Proxy Feature Detector. An ABBA FD whose condition reflects that of another FD to which it refers. Used to realise deictic reference.

Ready

A CM is ready if all its preconditions are satisfied.

SOM

Kohonen Self Organising Map [Kohonen, 1990].

Substance dualism

Mind-body dualism.

Sub-symbolic

Involving only iconic or indexical reference. ABBA provides a substrate for iconic and indexical reference, hence is sub-symbolic. It is possible for symbol systems to be layer above it, but no method for achieving this is now known.

Symbolic reference

Representation of associations between sets of other associations (iconic, indexical or symbolic). As symbolic references can represent associations between other symbols, a hierarchical representation system is possible. All symbols are ultimately grounded to iconic references – which is what gives them their meaning.

Theory of mind

Awareness of the perspective of others.

Token

A label for a symbol (often linguistic). Carries no intrinsic meaning. Tokens can only be meaningful when identified with their associated symbol, enabling re-grounding. Sometimes known as a sign.

Word

A spoken or written token.

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Appendix A CDROM The accompanying CDROM includes the following material. • Electronic versions of this Thesis (Adobe PDF and postscript) • The Yamabico Robot manual • ABBA source code • Some video footage that was prepared for various conferences – showing some of the component behaviours at work (MPEG format) The CD-ROM format is ISO9960 – with long file-names. It should be easily read from Microsoft Windows, MacOS or UNIX.

To access, insert the CD and load the thesis.html file into a Web browser, such as Netscape Communicator or Microsoft Internet Explorer. An MPEG player and Adobe PDF viewer may also be necessary.

Please obtain my permission before distributing copies of the CD or the documents it contains. The ABBA source-code may be used in any way, but I take no responsibility for its use.

- David Jung. WWW: http://pobox.com/~david.jung E-Mail: [email protected]

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