Abstract
Adaptability is a fundamental property of any intelligent system. In this paper, we present how adaptability in multi-agent systems can be implemented by means of collaborative logic-based learning. The proposed method is based on two building blocks: (1) a set of operations centred around inductive logic programming for generalizing agents’ observations into sets of rules, and (2) a set of communication strategies for sharing acquired knowledge among agents in order to improve the collaborative learning process. Using these modular building blocks, several learning algorithms can be constructed with different trade-offs between the quality of learning, computation and communication requirements, and the disclosure of the agent’s private information. The method has been implemented as a modular software component that can be integrated into the control loop of an intelligent agent. The method has been evaluated on a simulated logistic scenario, in which teams of trading agents learn the properties of the environment in order to optimize their operation.
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Foltýn, L., Tožička, J., Rollo, M., Pěchouček, M., Jisl, P.: Reflective-cognitive architecture: From abstract concept to self-adapting agent. In: DIS 2006. Proceedings of the Workshop on Distributed Intelligent Systems. IEEE Comp. Soc., Los Alamitos (2006)
Šišlák, D., Rehǎk, M., Pěchouček, M., Rollo, M., Pavlíček, D.: A-globed: Agent development platform with inaccessibility and mobility support. In: Unland, R., Klusch, M., Calisti, M. (eds.) Software Agent-Based Applications, Platforms and Development Kits, pp. 21–46. Birkhauser Verlag, Berlin (2005)
Kudenko, D., Kazakov, D., Alonso, E. (eds.): AAMAS 2004. LNCS (LNAI), vol. 3394. Springer, Heidelberg (2005)
Provost, F.J., Hennessy, D.N.: Scaling up: Distributed machine learning with cooperation. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence and the Eighth Innovative Applications of Artificial Intelligence Conference, pp. 74–79. AAAI Press / MIT Press, (1996)
Colton, S., Bundy, A., Walsh, T.: Agent based cooperative theory formation in pure mathematics. In: Wiggins, G. (ed.) Proceedings of AISB 2000 Symposium on Creative and Cultural Aspects and Applications of AI and Cognitive Science, pp. 11–18 (2000)
Panait, L., Luke, S.: Cooperative multi-agent learning: The state of the art. Autonomous Agents and Multi-Agent Systems 11, 387–434 (2005)
Kazakov, D., Kudenko, D.: Machine learning and inductive logic programming for multi-agent systems. In: Luck, M., Mařík, V., Štěpánková, O., Trappl, R. (eds.) ACAI 2001 and EASSS 2001. LNCS (LNAI), vol. 2086, pp. 246–270. Springer, Heidelberg (2001)
Guerra-Hernandez, A., Fallah-Seghrouchni, A., Soldano, H.: Learning in BDI multi-agent systems. In: Proceedings of CLIMA 2003, pp. 185–200 (2004)
Bartlett, M., Kazakov, D.: Comparing resource sharing with information exchange in co-operative agents, and the role of environment structure. In: Kudenko, D., Kazakov, D., Alonso, E. (eds.) Adaptive Agents and Multi-Agent Systems II. LNCS (LNAI), vol. 3394, pp. 41–54. Springer, Heidelberg (2005)
Alonso, E., d’Inverno, M., Kudenko, D., Luck, M., Noble, J.: Learning in multi-agent systems. Knowledge Engineering Review 16, 277–284 (2001)
Muggleton, S., Raedt, L.D.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19/20, 629–679 (1994)
Beckett, D.: RDF/XML syntax specification. Technical report, W3C (2004)
Lee, J.K., Sohn, M.M.: The extensible rule markup language. Communications of the ACM 46, 59–64 (2003)
Lavrač, N., Železný, F.: Propositionalization-based relational subgroup discovery with rsd. Machine Learning 62(1-2), 33–63 (2006)
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Jakob, M., Tožička, J., Pěchouček, M. (2008). Collaborative Learning with Logic-Based Models. In: Tuyls, K., Nowe, A., Guessoum, Z., Kudenko, D. (eds) Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning. AAMAS ALAMAS ALAMAS 2005 2007 2006. Lecture Notes in Computer Science(), vol 4865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77949-0_8
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DOI: https://doi.org/10.1007/978-3-540-77949-0_8
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