Norms and artificial agents Harko Verhagen Department of Computer and Systems Sciences The Royal Institute of Technology and Stockholm University Forum 100, SE-16440 Kista, Sweden
[email protected] November 28, 2001 In this section I will first describe the various uses of the word “norm” in different scientific arenas before stating the view I use. After this I will take a closer look at the work on norms I have conducted for this thesis. But first let us once again take a look at the Webster online dictionary: 1. an authoritative standard (model) 2. a principle of right action binding upon the members of a group and serving to guide, control, or regulate proper and acceptable behavior 3. average as: (a) a set standard of development or achievement usually derived from the average or median achievement of a large group (b) a pattern or trait taken to be typical in the behavior of a social group (c) a widespread practice, procedure, or custom (rule) These different views on norms are reflected in the different views on norms in different scientific disciplines. For my purposes I will describe the views on norms in legal theory, (social) psychology, (social) philosophy, sociology, and decision theory. Where appropriate I will also describe different views even within these disciplines. After the presentation of these different views, a working definition is presented and the learning of norms is addressed.
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Norms in Social Theory
In the description of the normative action model, Habermas [Hab84] identifies the use of norms in human action patterns as normatively regulated action. The central concept of complying with a norm means fulfilling a generalized expectation of behavior. The latter does not have the cognitive sense of expecting a predicted event, but the normative sense that members are entitled to expect a certain behavior. This normative model of action lies behind the role theory that is widespread in sociology ([Hab84] p.85, original emphasis). This view is in agreement with Tuomela [Tuo95], who distinguishes two kinds of social norms (meaning community norms), viz. rules (r-norms) and proper social norms (s-norms). Rules are norms created by an authority structure and are always based on agreement-making. Proper social norms are based on mutual belief. Rules can be formal, in which case they are connected to formal sanctions, or informal where the sanctions are also informal. Proper social norms consist of conventions, which apply to a large group such as a whole human society or socio-economic class, and group-specific norms. The sanctions connected to both types of proper social norms are social sanctions, and may include punishment by others and expelling from the group. Aside from these norms, Tuomela also describes personal norms and also potential social norms (these are norms which are normally widely obeyed but which are not in their essence based on “social responsiveness” and which in principle could be personal only). These potential social norms contain among others moral and prudential norms (m-norms and p-norms respectively). The reasons for accepting norms differ as to the kind of norms: • rules are obeyed since they are agreed upon • proper social norms are obeyed since others expect one to obey • moral norms are obeyed because of one’s conscience • prudential norms are obeyed because it is the rational thing to do The motivational power of all types of norms depends on the norm being a subject’s reason for action. In other words, norms need to be “internalized” and “accepted”.
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Norms in Legal Theory
Within deontic logic, a norm is viewed as an expression of the obligations and rights connected to the role an individual has within a larger social system. This is the second of the definitions taken from the Webster dictionary. The legal theory view on norms corresponds with Tuomela’s r-norms and are backed by formal sanctions. The different schools in legal theory do not differ on the definition of a norm but do differ on the mental dimensions of norms, i.e. on why agents accept and obey norms. In [CFS99] an overview of these different views is presented. The following reasons for norm accepting and obeying are given: • norms are accepted out of fear for the authority issuing the norm • norms are accepted since they are rational • norms are accepted from a sense of duty • norms are accepted since they solve problems of coordination and cooperation I will leave it to the reader to depict these upon the reasons for obeying as developed by Tuomela and described in the previous subsection.
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Multiagent Systems Research and Norms
The use of norms in artificial agents is a fairly recent development in multiagent systems research (c.f. e.g., [ST92], [VS97], [Bom99]). Even within multiagent systems research different definitions of norms are used. In [CC95] (pp. 91-92) the following views on norms in multiagent system research are described: • norms as constraints on behavior • norms as ends (or goals) • norms as obligations Most research on norms in multiagent systems focuses on norms as constraints on behavior via social laws (c.f. e.g. [BC95], [Mal96], [ST92]). These social laws are designed off-line1 and agents are not allowed to deviate from 1
In a recent article [ST97], social laws and conventions are not designed off-line but emerge at runtime. Social conventions limit the agent’s set of choices to exactly one. The agents are not allowed to deviate from the social laws or conventions. Furthermore, a central authority forces agents to comply.
4 the social laws (except in the work by Briggs, see below). In this sense the social laws are even more strict than the r-norms Tuomela describes which come closest to these social laws. The social laws are designed to avoid problems caused by interacting autonomous selfish agents, thus improving cooperation and coordination by constraining the agents’ action choices. This view on norms is based on the view on norms as developed within game theoretical research such as [UM77]. In [BC95], agents may choose less restrictive sets of social laws if they can not find a solution under a set of social laws, thus introducing a possibility for deviation. This approach is close to the approach in [Bom99] where sets of norms are used by an artificial agent decision support system (pronouncer ) to reorder decision trees with the agent having the possibility to refrain from using the reordered decision tree. The reasons behind this are not further developed in [Bom99], in contrast to [BC95]. However, the title of Briggs and Cook’s article Flexible social laws is deceiving, it is not the laws that are flexible, it is the way they are applied. The laws do not change, it is the agent who decides to apply them or not. The agent is only allowed to deviate from a social law if it cannot act. Thus the authors deny that not acting can be a choice and disconnect the choice of applying a social law from more realistic reasons other than the possibility to act. Work on cognitive grounded norms is conducted in the group around Castelfranchi and Conte (c.f., e.g., [CC95], [CCD99], [CFS99]) or in research inspired by their work (c.f., e.g., [SH99]). In [CCD99] norms are seen as indispensable for fully autonomous agents. The capacity for norm-acceptance is taken to depend upon the ability to recognize norms, normative authorities and on solving conflicts among norms. Since normative authorities are only of importance in the case of r-norms, the agents should also be able to recognize group members to be able to deal with s-norms. In [Tuo95] a theory solving conflicts among norms of different categories is developed that can complement the research described in [CCD99]. The origins of norms is not clarified in [CCD99]. However, the possibility of norm deviation is an important addition to multiagent systems research on norms.
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Working Definition of Norms
In the current work agents are viewed as having personal norms and coalition norms. The coalition norms are subjective, thus every agent has an individual view on each norm of the coalition. The personal norms emerge from the interaction with the environment. The coalition norms emerge from interaction with the other agents. This synchronization process will result in coalition norms that are shared by all agents and will lie somewhere between the set of personal norms of the individual agents comprising the coalition
REFERENCES
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(this averaging type of norm evolution is commonplace in human subjects [Bro90]). This final state will only be reached if the group does not change and if the individual norms have stabilized.
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Learning of Norms
The learning of norms can be divided in two types, viz. the emergence of norms (c.f. e.g. [UM77] for a description of the emergence of norms from a game theory point of view) and the acceptance of norms. These two types of learning express learning at different levels. The emergence of norms is learning at the level of the social system while the acceptance of norms is learning at the level of the individual agent. Reasons for accepting norms are discussed in the above subsection on Tuomela and the subsection on norms in legal theory. In [CCD99] reasons for the acceptance of norms in multiagent systems are discussed. I am not primarily interested in why agents accept norms. Instead I focus on how acceptance of norms changes the decision making behavior of the agents by changing the agent’s definition of the norms of the coalition (norm-spreading) and by the adaption of the agent’s own norms (norm-internalizing).
References [BC95]
W. Briggs and D. Cook. Flexible Social Laws. In Proceedings of the 1995 International Joint Conferences on Artificial Intelligence, pages 688–693. Morgan Kaufmann, 1995.
[Bom99] M. Boman. Norms in Artificial Decision Making. Artificial Intelligence and Law, 7(1):17–35, 1999. [Bro90]
R. Brown. Group Processes: Dynamics Within and Between Groups. Basil Blackwell, Cambridge, MA., 1990.
[CC95]
R. Conte and C. Castelfranchi. Cognitive and social action. UCL Press London, 1995.
[CCD99] R. Conte, C. Castelfranchi, and F. Dignum. Autonomous NormAcceptance. In Intelligent Agent V: Proceedings of ATAL 98, 1999. [CFS99] R. Conte, R. Falcone, and G. Sartor. Introduction: Agents and Norms: How to Fill the Gap? Artificial Intelligence and Law, pages 1–15, 1999.
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[Hab84] J. Habermas. The Theory of Communicative Action, Volume One, Reason and the Rationalization of Society. Beacon Press, Boston, 1984. transl McCarthy, orig publ as Theorie des Kommunikativen Handels, 1981. [Mal96] A.D. Mali. Social Laws for Agent Modeling. In M. Tambe and P. Gmytrasiewicz, editors, Agent Modeling Papers from the AAAI Workshop, pages 53–60. AAAI Press, 1996. [SH99]
N.J. Saam and A. Harrer. Simulating Norms, Social Inequality, and Functional Change in Artificial Societies. Journal of Artificial Societies and Social Simulation, 2(1), 1999. www.soc.surrey.ac.uk/JASSS/2/1/2.html.
[ST92]
Y. Shoham and M. Tennenholtz. On the Synthesis of Useful Social Laws for Artificial Agent Societies (Preliminary Report). In Proceedings of the National Conference on Artificial Intelligence, pages 276–281, San Jose, CA, July 1992.
[ST97]
Y. Shoham and M. Tennenholtz. On the Emergence of Social Conventions: modeling, analysis, and simulations. Artificial Intelligence, 94(1-2):139–166, 1997.
[Tuo95] R. Tuomela. The Importance of Us: A Philosophical Study of Basic Social Norms. Stanford University Press, 1995. [UM77]
E. Ullman-Margalit. The Emergence of Norms. Clarendon Press, 1977.
[VS97]
H.J.E. Verhagen and R.A. Smit. Multiagent Systems as Simulation Tools for Social Theory Testing. Paper presented at poster session at ICCS and SS Siena, 1997.
