Abstract
This paper presents a model of a learning mechanism for situated agents. The learning is described explicitly in terms of plans and conducted as intentional actions within the BDI (Beliefs, Desires, Intentions) agent model. Actions of learning direct the task-level performance towards improvements or some learning goals. The agent is capable of modifying its own plans through a set of actions on the run. The use of domain independent patterns of actions is introduced as a strategy for constraining the search for the appropriate structure of plans. The model is demonstrated to represent Q-learning algorithm, however different variation of pattern can enhance the learning.
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References
Bratman, M.E.: Intention, Plans and Practical Reason. Harvard University Press, Cambridge (1987)
Cohen, P.R., Levesque, H.J.: Persistence, intention, and commitment. In: Cohen, P.R., Morgan, J., Pollack, M.E. (eds.) Intentions in Communication, pp. 33–69. MIT Press, Cambridge (1990)
Hernandez, G., Segrouchini, A.E., Soldano, H.: BDI multiagent learning based on first-order induction of logical decision trees. In: Oshuga, S., Zhong, N., Liu, J., Bradshaw, J. (eds.) Intelligent Agent Technology: Research and Development, World Scientific, New Jersey (2001)
Howden, N., Ronnquist, R., Hodgson, A., Lucas, A.: Jack intelligent agents-summary of an agent infrastructure. In: Proceedings of the 5th International Conference on Autonomous Agents, Montreal (2001)
Huber, M.J.: Jam: A BDI-theoretic mobile agent architecture. In: Proceedings of the Third International Conference on Autonomous Agents, Seattle (1999)
Ingrand, F., Georgeff, M., Rao, A.: An architecture for real-time reasoning and system control. IEEE Expert 7(6), 34–44 (1992)
Mitchell, T.: Machine Learning. McGraw-Hill, Cambridge (1997)
Olivia, C., Chang, C.-F., Enguix, C.F., Ghose, A.K.: Casebased bdi agents: an effective approach for intelligent search on the world wide web. In: Proceedings of the AAAI 1999, Spring Symposium on Intelligent Agents in Cyberspace Stanford University, USA (1999)
Rao, A.S., Georgeff, M.P.: BDI agents: From theory to practice. In: Proceedings of the First International Conference on Multi-Agent Systems (ICMAS 1995), San Francisco (1995)
Rao, A.S., Georgeff, M.P.: Modeling rational agents within a bdi-architecture. In: Fikes, R., Sandewall, E. (eds.) Proceedings of Knowledge Representation and Reasoning, San Mateo, pp. 473–484. Morgan Kaufmann, San Francisco (1991)
Subagdja, B., Sonenberg, L.: Reactive planning through reflective hypotheses: Towards learning in BDI agents. Submitted for AAMAS (2005)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Wooldridge, M.: Reasoning about Rational Agents. MIT Press, Cambridge (2000)
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Subagdja, B., Sonenberg, L. (2005). Learning Plans with Patterns of Actions in Bounded-Rational Agents. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_5
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DOI: https://doi.org/10.1007/11553939_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28896-1
Online ISBN: 978-3-540-31990-0
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