A strategy learning model for autonomous agents based on classification
International Journal of Applied Mathematics and Computer Science (2015)
- Volume: 25, Issue: 3, page 471-482
- ISSN: 1641-876X
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top- Airiau, S., Padham, L., Sardina, S. and Sen, S. (2008). Incorporating learning in BDI agents, Proceedings of the ALAMAS+ALAg Workshop, Estoril, Portugal.
- Barrett, S., Stone, P., Kraus, S. and Rosenfeld, A. (2012). Learning teammate models for ad hoc teamwork, AAMAS Adaptive Learning Agents (ALA) Workshop, Valencia, Spain.
- Bazzan, A., Peleteiro, A. and Burguillo, J. (2011). Learning to cooperate in the iterated prisoners dilemma by means of social attachments, Journal of the Brazilian Computer Society 17(3): 163-174.
- Bellman, R. (1957). Dynamic Programming, A Rand Corporation Research Study, Princeton University Press, Princeton, NJ.
- Cetnarowicz, K. and Drezewski, R. (2010). Maintaining functional integrity in multi-agent systems for resource allocation, Computing and Informatics 29(6): 947-973.
- Cohen, W.W. (1995). Fast effective rule induction, Proceedings of the 12th International Conference on Machine Learning (ICML'95), Tahoe City, CA, USA, pp. 115-123.
- Dietterich, T.G. (2000). Hierarchical reinforcement learning with the MAXQ value function decomposition, Journal of Artificial Intelligence Research 13: 227-303. Zbl0963.68085
- Gehrke, J.D. and Wojtusiak, J. (2008). Traffic prediction for agent route planning, in M. Bubak et al. (Eds.), Computational Science-ICCS 2008, Part III, Lecture Notes Computer Science, Vol. 5103, Springer, Berlin/Heidelberg, pp. 692-701.
- Hernandez-Leal, P., Munoz de Cote, E. and Sucar, L.E. (2013). Learning against non-stationary opponents, Workshop on Adaptive Learning Agents, Saint Paul, MN, USA.
- Kaelbling, L.P., Littman, M.L. and Moore, A.W. (1996). Reinforcement learning: A survey, Journal of Artificial Intelligence Research 4: 237-285.
- Kazakov, D. and Kudenko, D. (2001). Machine learning and inductive logic programming for multi-agent systems, in M. Luck et al. (Eds.), Multi-Agent Systems and Applications, Springer, Berlin/Heidelberg, pp. 246-270. Zbl0989.68553
- Lin, L.-J. (1992). Self-improving reactive agents based on reinforcement learning, planning and teaching, Machine Learning 8(3-4): 293-321.
- Panait, L. and Luke, S. (2005). Cooperative multi-agent learning: The state of the art, Autonomous Agents and Multi-Agent Systems 11(3): 387-434.
- Quinlan, J. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, CA.
- Rao, A.S. and Georgeff, M.P. (1991). Modeling rational agents within a BDI-architecture, in J. Allen, R. Fikes and E. Sandewall (Eds.), Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning, Morgan Kaufmann: San Mateo, CA, pp. 473-484. Zbl0765.68194
- Rummery, G.A. and Niranjan, M. (1994). On-line q-learning using connectionist systems, Technical report, Cambridge University, Cambridge.
- Russell, S.J. and Zimdars, A. (2003). Q-decomposition for reinforcement learning agents, Proceedings of the 20th International Conference on Machine Learning (ICML2003), Washington, DC, USA, pp. 656-663.
- Russell, S. and Norvig, P. (2009). Artificial Intelligence: A Modern Approach, 3rd Edn., Prentice-Hall, Upper Saddle River, NJ. Zbl0835.68093
- Sen, S. and Weiss, G. (1999). Learning in Multiagent Systems, MIT Press, Cambridge, MA, pp. 259-298.
- Shoham, Y., Powers, R. and Grenager, T. (2003). Multi-agent reinforcement learning: A critical survey, Technical report, Stanford University, Stanford, CA. Zbl1168.68493
- Singh, D., Sardina, S., Padgham, L. and Airiau, S. (2010). Learning context conditions for BDI plan selection, Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems, Toronto, Canada, pp. 325-332.
- Śnieżyński, B. (2013a). Agent strategy generation by rule induction, Computing and Informatics 32(5): 1055-1078.
- Śnieżyński, B. (2013b). Comparison of reinforcement and supervised learning methods in farmer-pest problem with delayed rewards, in C. Badica, N.T. Nguyen and M. Brezovan (Eds.), Computational Collective Intelligence, Lecture Notes in Computer Science, Vol. 8083, Springer, Berlin/Heidelberg, pp. 399-408.
- Śnieżyński, B. (2014). Agent-based adaptation system for service-oriented architectures using supervised learning, Procedia Computer Science 29: 1057-1067.
- Śnieżyński, B. and Dajda, J. (2013). Comparison of strategy learning methods in farmer-pest problem for various complexity environments without delays, Journal of Computational Science 4(3): 144-151.
- Śnieżyński, B. and Kozlak, J. (2005). Learning in a multi-agent approach to a fish bank game, in M. Pchouek, P. Petta and L.Z. Varga (Eds.), Multi-Agent Systems and Applications IV, Lecture Notes in Computer Science, Vol. 3690, Springer, Berlin/Heidelberg, pp. 568-571.
- Śnieżyński, B., Wojcik, W., Gehrke, J.D. and Wojtusiak, J. (2010). Combining rule induction and reinforcement learning: An agent-based vehicle routing, Proceedings of the International Conference on Machine Learning and Applications, Washington, DC, USA, pp. 851-856.
- Sutton, R. and Barto, A. (1998). Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning), The MIT Press, Cambridge, MA.
- Sutton, R.S. (1990). Integrated architecture for learning, planning, and reacting based on approximating dynamic programming, Proceedings of the 7th International Conference on Machine Learning, Austin, TX, USA, pp. 216-224.
- Tan, M. (1993). Multi-agent reinforcement learning: Independent vs. cooperative agents, Proceedings of the 10th International Conference on Machine Learning, Amherst, MA, USA, pp. 330-337.
- Tuyls, K. and Weiss, G. (2012). Multiagent learning: Basics, challenges, and prospects, AI Magazine 33(3): 41-52.
- Watkins, C.J.C.H. (1989). Learning from Delayed Rewards, Ph.D. thesis, King's College, Cambridge.
- Wooldridge, M. (2009). An Introduction to MultiAgent Systems, 2nd Edn., Wiley Publishing, Chichester.
- Zhang, W. and Dietterich, T.G. (1995). A reinforcement learning approach to job-shop scheduling, Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, Canada, pp. 1114-1120.