Abstract
The principal aim of this research is to structure agents according to different situations that have to deal with. To achieve this, we propose a conceptual model with a hierarchical structure defined by the skill-rule-knowledge (S — R — K) levels. At the skill level, the agent deals with routines and her behavior is governed by stored patterns of predefined procedures, that map directly an observation (i.e. perception) to an action. The rule-based level represents more conscious behavior and it deals with familiar situations. This behavior is generally conventionally described by a set of heuristics. Finally, the knowledge-based level accounts for unfamiliar situations for which know-how or rules are not available.
An implementation of this model has been done in the context of a multiagent environment to confirm our mean expectation: the perceptual processing (i.e., S and R levels) is fast, effortless and is propitious for coordinated activities between agents, whereas the analytical problem solving (i.e., K level) is slow, laborious and can lead to conflicts between agents.
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References
P. Agre and D. Chapman, Penguin: an implementation of a theory of activity, Proc. of AAAI-87, Seattle WA (1987) 268–272.
J. S. Albus, Outline for a theory of intelligence, IEEE Trans. Syst., Man, Cybern., vol. SMC-21 (1991) 473–509.
R. Axelrod (ed.), Structure of Decision: The Cognitive Maps of Political Elites, Princeton University Press (1976).
D. G. Bobrow (ed.), Qualitative Reasoning about Physical Systems, MIT Press, (1985).
B. Chaib-draa, Coordination between agents in routine, familiar, and unfamiliar situations, Int. Jour, of Intelligent & Cooperative Information Systems, 1996, (to appear).
B. Chaib-draa, and P. Levesque, Hierarchical Model and Communication By Signs, Signals and Symbols in Multiagent Environments, Journal of Experimental and Theoretic Artificial Intelligence, 1996, (to appear).
P. R. Cohen, M. L. Greenberg, D. H. Hart and A. E. Howe, Trial by fire: understanding the design requirements for agents in complex environments, AI Magazine, vol. 10 (1989) 34–28.
E. H. Durfee and T. A. Montgomery, Coordination as distributed search in a hierarchical behavior space, IEEE Trans. Syst., Man, Cybern, vol. SMC-21 (1991) 1363–1378.
I. A. Ferguson, Touring-Machines: autonomous agents with attitudes, IEEE Computer, vol. 25 (1992).
I. A. Ferguson, Touring-Machines: An architecture for Dynamic, Rational, Mobile Agents., Ph.D. thesis, Computer Lab. Cambridge University, Cambridge UK, 1992.
J. R. Firby, An investigation into reactive planning in complex domains, Proc. of AAAI-87, Seattle, WA (1987) 202–206.
M. P. Georgeff and A. L. Lansky, Reactive reasoning and planning. Proc. of the Sixth Nat. Conf. on AI (AAA-87), (1987).
J. L. Kolodner, An introduction to case-based reasoning, Artificial Intelligence Review, 6(1) (1992) 3–34.
J. P. Müller and M. Pishel, An architecture for dynamically interacting agents, Int. Jour, of Intelligent and Cooperative Systems, 3(1), (1994), 25–45.
A. Newell, The Knowledge Level, Artificial Intelligence, 18(1), (1982), 87–127.
J. Rasmussen. Information Processing and Human-Machine Interaction: An Approach to Cognitive Engineering, North Holland (1986).
J. Reason, Cognitive aids in process environments: prostheses or tools?, Cognitive Engineering in Complex Dynamic Worlds, E. Hollnagel, G. Mancini, and D. d. Woods, eds., Academic Press, London, (1988).
J. Reason, Human Error. Cambridge, UK: Cambridge Univ. Press (1990).
C. K. Riesbeck and R. C. Schank, Inside case-based reasoning, Lawrence Elbaum associates, Publishers, Hillsdale, New Jersey (1989).
A. P. Sage, Information systems engineering for distributed decisionmaking, IEEE Trans. Syst., Man, Cybern., vol. SMC-17 (1987) 920–936.
R. C. Schank, Dynamic Memory: A Theory of Reminding and Learning in Computers and People, Cambridge University Press (1982).
W. R. Zhang, S. S. Chen, and R. S. King, A cognitive map based approach to the coordination of distributed cooperative agents, IEEE Trans. Syst., Man, Cybern., vol. SMC-22 (1992) 103–113.
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© 1996 Springer-Verlag Berlin Heidelberg
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Chaib-draa, B. (1996). A hierarchical model of agent based on skill, rules, and knowledge. In: McCalla, G. (eds) Advances in Artifical Intelligence. Canadian AI 1996. Lecture Notes in Computer Science, vol 1081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61291-2_52
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DOI: https://doi.org/10.1007/3-540-61291-2_52
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