Abstract
We present an algorithm that an agent can use for determining which of its nested, recursive models of other agents are important to consider when choosing an action. Pruning away less important models allows an agent to take its “best” action in a timely manner, given its knowledge, computational capabilities, and time constraints. We describe a theoretical framework, based on situations, for talking about recursive agent models and the strategies and expected strategies associated with them. This framework allows us to rigorously define the gain of continuing deliberation versus taking action. The expected gain of computational actions is used to guide the pruning of the nested model structure. We have implemented our approach on a canonical multi-agent problem, the pursuit task, to illustrate how real-time, multi-agent decision-making can be based on a principled, combinatorial model. Test results show a marked decrease in deliberation time while maintaining a good performance level.
Supported, in part, by NSF grant IRI-9158473.
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References
Edmund H. Durfee. Blissful ignorance: Knowing just enough to coordinate well. In Proceedings of the First International Conference on Multi-Agent Systems, pages 406–13, 1995.
Edmund H. Durfee, Piotr J. Gmytrasiewicz, and Jeffrey S. Rosenschein. The utility of embedded communications and the emergence of protocols. In Proceedings of the 13th International Distributed Artificial Intelligence Workshop, 1994.
Edmund H. Durfee, Jaeho Lee, and Piotr J. Gmytrasiewicz. Overeager reciprocal rationality and mixed strategy equilibria. In Proceedings of the eleventh National Conference on Artificial Intelligence, 1993.
Ronald Fagin, Joseph Y. Halpern, Yoram Moses, and Moshe Y. Vardi. Reasoning About Knowledge. MIT Press, 1995.
Les Gasser, Nicholas F. Rouquetter, Randall W. Hill, and John Lieb. Representing and using organizational knowledge in distributed ai systems. In Les Gasser and Michael N Huhns, editors, Distributed Artificial Intelligence, volume 2, pages 55–78. Morgan Kauffman Publishers, 1989.
Piotr J. Gmytrasiewics and Edmund H. Durfee. A rigorous, operational formalization of recursive modeling. In Proceedings of the First International Conference on Multi-Agent Systems, pages 125–132, 1995.
Piotr J. Gmytrasiewicz. A Decision-Theoretic Model of Coordination and Communication in Autonomous Systems (Reasoning Systems). PhD thesis, University of Michigan, 1992.
Piotr. J. Gmytrasiewicz. On reasoning about other agents. In M. Wooldridge, J. P. Müller, and M. Tambe, editors, Intelligent Agents Volume II — Proceedings of the 1995 Workshop on Agent Theories, Architectures, and Languages (ATAL-95), Lecture Notes in Artificial Intelligence. Springer-Verlag, 1996. (In this volume).
Piotr J. Gmytrasiewicz and Edmund H. Durfee. Toward a theory of honesty and trust among communicating autonomous agents. Group Decision and Negotiation, 2:237–258, 1993.
Richard E. Korf. A simple solution to pursuit games. In Proceedings of the 11th International Distributed Artificial Intelligence Workshop, 1992.
Ran Levy and Jeffrey S. Rosenschein. A game theoretic approach to the pursuit problem. In Proceedings of the 11th International Distributed Artificial Intelligence Workshop, 1992.
T. A. Montgomery and Edmund H. Durfee. Using mice to study intelligent dynamic coordination. In Proceedings of IEEE Conference on Tools for AI, 1990.
Anand S. Rao and Michael P. Georgeff. BDI agents: From theory to practice. In Proceedings of the First International Conference on Multi-Agent Systems, pages 312–319, 1995.
Stuart Russell and Eric Wefald. Do The Right Thing. The MIT Press, Cambridge, Massachusetts, 1991.
Sandip Sen and Edmund H. Durfee. Adaptive surrogate agents. In Proceedings of the 13th International Distributed Artificial Intelligence Workshop, 1994.
Herbert A. Simon. Models of Bounded Rationality, volume 2. MIT Press, 1982.
Larry M. Stephens and Matthias B. Merx. The effect of agent control strategy on the performance of a DAI pursuit problem. In Proceedings of the 9th International Distributed Artificial Intelligence Workshop, 1990.
M. Tambe and P. S. Rosenbloom. Agent tracking in real-time dynamic environments. In M. Wooldridge, J. P. Müller, and M. Tambe, editors, Intelligent Agents Volume II — Proceedings of the 1995 Workshop on Agent Theories, Architectures, and Languages (ATAL-95), Lecture Notes in Artificial Intelligence. Springer-Verlag, 1996. (In this volume).
José M. Vidal and Edmund H. Durfee. Agent modeling methods using limited rationality. In Proceedings of the Twelfth National Conference on Artificial Intelligence, page 1495, 1994.
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Vidal, J.M., Durfee, E.H. (1996). Using recursive agent models effectively. In: Wooldridge, M., Müller, J.P., Tambe, M. (eds) Intelligent Agents II Agent Theories, Architectures, and Languages. ATAL 1995. Lecture Notes in Computer Science, vol 1037. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3540608052_66
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DOI: https://doi.org/10.1007/3540608052_66
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