Zafar Iqbal Hashmi, Yu-N Cheah, Syed Zahid Hassan and Kee Guan Lim ... Syed Sibte Raza Abidi. Faculty of Computer ..... [12] Z.I.Hashami, S.S.R.Abidi and S.Z.H. Zaidi, 2002, Intelligent Agent Based Knowledge Assistant for Enterprise-Wide.
INTELLIGENT AGENT MODELING AND GENERIC ARCHITECTURE TOWARDS A MULTI-AGENT HEALTHCARE KNOWLEDGE MANAGEMENT SYSTEM Zafar Iqbal Hashmi, Yu-N Cheah, Syed Zahid Hassan and Kee Guan Lim Health Informatics Research Group, School of Computer Sciences Universiti Sains Malaysia, 11800 Penang, Malaysia
Syed Sibte Raza Abidi Faculty of Computer Science, Dalhousie University Halifax B3H 1W5, Canada
ABSTRACT Intelligent agents are a powerful technology with many significant applications and are starting to assume more responsible in more critical tasks. One of the most important uses of agent technology is for problem–solving coordination. Coordination has been defined as managing the interdependencies between activities, ordering and locating actions in time in an attempt to maximize a possibly changing set of decision criteria. Coordination activities include not only localized agent interaction over specific problems, but also longer term organizations that can support current and future problem solving. That is why to deploy intelligent agents in a multi-agent system in an effective manner, so that agents could achieve their objectives and could initiate collaboration and coordination with each other, there is a need to develop a generic agent architecture to build agents of different nature for a multi-agent system. Therefore, to come up with a state of the art solution for this issue, in this paper, we present: (i) Agent modeling (ii) Intelligent agent generic architecture and (iii) Healthcare Knowledge Management system (HKMS) implementation based on a multi-agent architectural framework to examine the efficacy of multi-agent systems in the healthcare domain. KEYWORDS
Intelligent agent, Agent architecture, Agent modeling, Multi-agent system.
1. INTRODUCTION Many of the recent, exciting developments in artificial intelligence (AI) have centered around the concept of intelligent agents [9]. Intelligent agents is a powerful technology which shows considerable promise as a new paradigm for state of the art intelligent system development. Agents offer new ways of abstraction, decomposition and organization that fit well in real world problems and can naturally model (human) organizations ranging from enterprise business structures to enterprise business processes. A precise and standard definition of an intelligent agent is forthcoming but the current working notion is that intelligent agents can be viewed as autonomous software (or hardware) constructs that are proactively involved in achieving a predetermined task and at the same time reacting to its environment. Intelligent agents are also social entities where they can communicate with other agents using an agent-communication language, (such as the Knowledge Query Manipulation Language (KQML)), in the process of carrying out their tasks [8][4]. As an agent is autonomous, it can be assigned tasks that can potentially be done more quickly, efficiently, safely, intelligently, and more decisively. To deploy agent technology for real world problem successfully, in this paper, we present the modeling and designing of intelligent agent architecture so that we could use them in our multi-agent framework for a healthcare knowledge management system for knowledge assistance.
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2. INTELLIGENT AGENT MODELING We design our agent model by a description of the basic concept of (1) Action, (2) Percepts and (3) Decision. (1) For an agent an action is something which an agent does, that is basically an agent’s ability to effect its environment [7]. In their simplest form actions are atomic, instantaneous; either fails or succeeds, can be durational (encompassing behavior over time) and can produce partial effects. Actions are also part of its interface to the environment. (2) A percept is an input for an agent through which the agent performs sensing actions and triggers some events to make some decision. (3) The essence of intelligent agent is rational decision making which makes decision based on rules defined for each specific domain in their knowledge base [3]. Based on these concepts, an agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors. So, our agent model design concepts are (i) Desires (ii) Brain (iii) Communication mechanism, (iv) Intentions (v) Belief (vii) Adaptation and (vi) Percepts. These agent modeling concepts are described as follows • Desires: These are the objectives to be accomplished. • Brain: Agent brain is a knowledge base which contains domain specific rules which are triggered to make action plans. • Communication: Agent communication involves messaging via standard communication languages e.g KQML to share global knowledge of it environment. • Intentions: The current chosen course of action. • Belief: Information about the environment. • Adaptation: It is a behavior of an agent in response to unexpected (i.e. low probability) events or dynamic environment. • Percepts: These are the sensors for an agent, which act as inputs from the environment.
3. INTELLIGENT AGENT GENERIC ARCHITECTURE The agent architecture designed here covers all the concepts defined in agent model described earlier and is general purpose in nature. It consists of seven components which are Event Scheduler, Effector, Knowledge base, Message Processor, Agent Name Server (ANS), Dispatcher and Shared Ontology. Each component performs its specific task to carry out agent specific goal. The agent generic architecture is shown in Figure 1. • Event Scheduler: The Event scheduler’s primary tasks are: (i) To capture events being carried out on/by the agent (ii) To evaluate event priority to determine a set of actions and keep track on all the events and (iii) To schedule and pass them to the effector. • Knowledge Base: There must be a well structured and defined knowledge about a specific domain and environmental knowledge for agents to perform its tasks successfully and intelligently. This knowledge is kept in forms of well structured rules into an agent’s local knowledge base. Rules are triggered and lead to specific plans which is executed to perform actions. Plans are executed through the effector. A plan is a way of realizing a goal [7][3]. • Effector: Once the agent has perceptively recognized that a significant event has occurred causing some rules to be fired to choose some plans, then the next step is to take some action according to the plans [3].These actions are performed by the effector which in turn contains a number of methods which are executed according to the chosen plan. • Message Processor: An agent needs to have some sort of communication mechanism and protocol so that it can communicate to enhance collaboration and be reactive. The Message processor deals with these functions. • Dispatcher: The Dispatcher deals with network protocols and handles incoming and outgoing messages. It receives and dispatches messages from/to respective agents using the ANS. • Agent Name Server (ANS):ANS acts like a DNS. It maintains the list of all agents which are part of multi-agent system and keeps track on all agents which are registered or unregistered. • Shared Ontology: As exchanging messages among the agents is part of communication issues, therefore, agents need to have a shared ontology [1].Here a shared ontology maintains all communication messages generic structure and respective meaning for each specific domain. These
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CLIENT
messages are built using agent communication languages, e.g, KQML etc. These generic messages from the shared ontology are then taken and constructed by Message Processor as per demand. Query
INTERFACE LAYER
Mobile Web Interface Agent Information
TRANSPORT LAYER
Shared Ontologies
Figure 1. Agent generic architecture
Presentation Agent
Agent Manager case broker
Scenario broker
Doc broker
KNOWLEDGE DESCRIPTION LAYER
AGENT APPLICATION LAYER
Query Optimization Agent
MEDICALONTOLOGY
Shared Ontologies
ANS
Dispatcher
Knowledge Base
Message Processor
SERVER
MEDICAL ONTOLOGY
Event Scedular
Effector
HEALTHCARE PRACTITIONERS
INTERNET / INTRANET
Retrieval unit
OBJECT LAYER
Healtcare Scenario Base
Clinical Case Base
Medical Document Base
HEALTHCARE ENTERPRISE MEMORY
Figure 2. System architecture
4. IMPLEMENTATION OF HKMS VIA MULTI-AGENTS HKMS is a framework ( see Figure 1) consisting of: (a) a healthcare knowledge web—which provides access paths to diverse knowledge sources; and (b) agent-mediated intelligent access to, and procurement of, heterogeneous knowledge by approximate matching of resources, content navigation, and content correlation. HKMS’s focused knowledge search and navigation is grounded in five fundamental principles: (i) it employs specific functionally-autonomous knowledge retrieval and procurement agents for each constituent repository; (ii) it employs a common ontology modeling the knowledge objects; (iii) it collects knowledge by leveraging a medical ontology that assists knowledge matching and adaptation; (iv) it populates the HEM from only those sources that need to be accessed for relevant content; and (v) it ensures inter-agent communication for agent collaboration to traverse the HEM for ‘holistic’ knowledge retrieval. HKMS is facilitated with seven independent autonomous intelligent agents. Each agent performs its tasks in an efficient manner to achieve the objective of the system. The beauty of this system is that all agents have similar architectures but perform different tasks for which they are built. These agents are built using the generic agent architecture. • Mobile Web Interface Agent (MWIA): This agent establishes a web-based interface at the client side via the internet/intranet [4][5] and provides the functionality to capture Knowledge Specifications from healthcare practitioners and sends those specification to the Agent Manager: • Agent Manager (AM): Agent Manager is the controlling body in HKMS framework. Its primary tasks are: (i) providing basic mediation services to all agents in the HKMS, (ii) coordinating these services according to given protocols, conventions, and policies (iii) ensuring reliable service mediation in terms of leveled quality of services as well as trust management within a multi-agent system border [9]. • Query Optimizing Agent (QOA): This agent receives user defined queries from AM and recomposes it by eliminating all unnecessary noise from queries and transforms them into the standard format useable by Intelligent Brokering Agents. • Intelligent brokering agent (IBA): As our HEM is multi-model in nature that is why the HKMS deploys three brokering agent which are: Document brokering agent, Case brokering agent and Scenario brokering agent. These brokering agent are similar in their architectural model but designed under their specific domain level knowledge. Each brokering agent’s primary function is to populate the healthcare enterprise memory (HEM) in an organized and structured manner according
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to specific knowledge representation scheme [12] and to retrieve just-in-time domain specific knowledge by autonomous navigation, approximate matching, and content co-relation based on the optimized queries from the QOA. After successfully retrieving valuable knowledge, each brokering agent submits them to presentation agents via message passing communication protocols.
5. CONCLUDING REMARKS In our work, we have discussed and identified some practical issues pertaining to modeling and designing of an agent generic architecture leading to the implementation of multi-agents for a healthcare knowledge management system. The main issues that we discussed are modeling and the importance of a generic architecture for intelligent agents which is the building block for the development of any kind of agent and multi-agent system. We presented the HKMS which is a multi-agent system for healthcare knowledge management to examine the efficiency and efficacy of this agent architecture.
REFRENCES [1] A.Aarsten, D.D. Brugali, and C. Vlad., 1996. Cooperating Among Autonomous Agents, Proceeding of 4th International Cconference on Control Automation, Roboticsand Vision Singapore, ,pp 3-6. [2] Chandrasekaran, B. Josephson, and J.R.Benjamins, 1999.What are ontologies, and why do we need them. IEEE Intelligent SYStems,14(1),pp.20-26. [3] J.P.Bigus, and J. Bigus , 2001. Constructing Intelligent Agents using Java.Robert Ipsen,USA. [4] J. Hendler, 1999, Making Sense out of Agents, IEEE Intelligent Systems, 14(2), pp. 32-37. [5] Kurbel, K.Szulim, and D.Teuteberg, 2000., XML-based Agent Communication for Electronic Marketplaces. Proceedings of ISA 2000 - Intelligent Systems and Applications. Wollongong, Australia.. [6] M.Klusch. and Sycara,2001,Brokering and matchmaking for coordination of agent societies: a survey,Coordination of Internet Agents:Models, Technologies, and Applications, Springer-Verlag, London, UK. [7] M.Winikoff, L.Padgham, and J.Harland, 2001, Simplifying the Development of Intelligent Agents. Proceedings of 14. Australian Joint Conference on Artificial Intelligence: Adelaide, Australia, 557-568. [8] M. Wooldridge, and N. R. Jennings, 1995, Intelligent Agents: Theory and Practice. Knowledge Engineering Review, 10(2). [9] Scerri, and P. Reed, 2001. Designing Agents for Systems with Adjustable Autonomy The IJCAI-01 Workshop on Autonomy, Delegation, and Control: Interacting with Autonomous Agents. [10] Yu-N. Cheah and S.S.R.Abidi, 2001, Augmenting Knowledge-Based Medical Systems with Tacit Healthcare Expertise: Towards an Intelligent Tacit Knowledge Acquisition Info-Structure. Fourteenth IEEE Symposium on Computer-Based Medical Systems (CBMS 2001), Bethesda, Maryland. [11]Z.I.Hashami, S.S.R.Abidi and Yu-N. Cheah , 2002. , Intelligent Healthcare Information Assistant: Towards Agent Based Healthcare Knowledge Management, The XVIIIth International Congress of the European Federation for Medical Informatics (MIE 02), Budapest, Hungary. [12] Z.I.Hashami, S.S.R.Abidi and S.Z.H. Zaidi, 2002, Intelligent Agent Based Knowledge Assistant for Enterprise-Wide Health Care Cooperative Knowledge-Factory". The First International Conference on "ICT Research & Applications: Innovations at Work", CITRA 2002, Kuala Lumpur, Malaysia, 25-26 September.
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