A Recursive Classifier System for Partially Observable Environments

• Autorzy: Hamzeh A. , Hashemi S. , Sami A. , Rahmani A.
• Języki publikacji: EN

Abstrakty

EN

Previously we introduced Parallel Specialized XCS (PSXCS), a distributed-architecture classifier system that detects aliased environmental states and assigns their handling to created subordinate XCS classifier systems. PSXCS uses a history-window approach, but with novel efficiency since the subordinateXCSs, which employ the windows, are only spawned for parts of the state space that are actually aliased. However, because the window lengths are finite and set manually, PSXCS may fail to be optimal in difficult test mazes. This paper introduces Recursive PSXCS (RPSXCS) that automatically spawns windows wherever more history is required. Experimental results show that RPSXCS is both more powerful and learns faster than PSXCS. The present research suggests new potential for history approaches to partially observable environments.

Słowa kluczowe

EN

Learning Classifier Systems; Partially Observable Environments; XCS; CSXCS

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2009
• Tom: Vol. 97, nr 1/2
• Strony: 15--40
• Opis fizyczny: Bibliogr. 28 poz., tab., wykr.

Twórcy

autor

Hamzeh A.

autor

Hashemi S.

autor

Sami A.

autor

Rahmani A.

• CSE and IT Department, Engineering Campus 2, Shiraz Univ., Mollasadra Ave., Shiraz, Iran,

Bibliografia

• [1] Bagnall, A.J., Zatuchna, Z. (2005). On the Classification of Maze Problems, Applications of Learning Classifier Systems, Studies in, Edited by Bull, L. and Kovacs, T., Springer, pp. 307-316.
• [2] Bull, L. (2002). Look ahead and latent learning in ZCS. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 897-904, New York, Morgan Kaufmann Publishers.
• [3] Butz, M.V. (2002). Biasing Exploration in an Anticipatory Learning Classifier System, In Advances in Learning Classifier Systems, volume 2321 of LNAI. Springer-Verlag, Berlin, pages 3-22.
• [4] Cliff, D., Ross, S. (1994). Adding Temporar yMemory to ZCS, Adaptive Behavior Journal 3(2), pages 101-150.
• [5] Gerard, P., Sigaud, O. (2001). Adding a Generalization Mechanism to YACS, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 951-957, San Francisco, California, USA, 7-11 July 2001. Morgan Kaufmann.
• [6] Hamzeh, A., Rahmani, A. (2008), A New Architecture for Learning Classifier Systems to Solve POMDP Problems, Fundamenta Informaticae, vol. 84(3-4), pp. 329-351, IOS Press.
• [7] Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, republished by the MIT press, 1992.
• [8] Holland, J.H. (1976), Adaptation, In R. Rosen and F. M. Snell, editors, Progress in Theoretical Biology. New York: Plenum.
• [9] Kanji, G. (1994). 100 Statistical Tests, SAGE Publications.
• [10] Kaelbling, L.P, Littman, M.L., Moore, A. (1996). Reinforcement Learning: A Survey, Artificial Intelligence Research, vol. 4, pages 237-285.
• [11] Kaelbling, L.P., Littman, M.L., Cassandra, A.R. (1998). Planning and Acting in Partially Observable Stochastic Domains, Artificial Intelligence, Vol. 101.
• [12] Lanzi, P.L. (1997). A Model of the Environment to Avoid Local Learning (An Analysis of the Generalization Mechanism of XCS), Technical Report 9746, Politecnico di Milano, Department of Electronic Engineering and Information Sciences.
• [13] Lanzi, P.L. (1997). A Model of the Environment to Avoid Local Learning, Technical Report Number 9746, Dipartimneto di Electronica e Informazione, Politectico di Milano.
• [14] Lanzi, P.L. (1998). Adding Memory to XCS, In Proceedings of the IEEE Conference on Evolutionary Computation, IEEE Press.
• [15] Lanzi, P.L., Colombetti M. (1999), An Extension to the XCS Classifier System for Stochastic Environments, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 353-360, Morgan Kaufmann, Orlando (FL).
• [16] Lanzi, P.L., Wilson, S.W. (2000). Toward Optimal Classifier System Performance in Non-Markov Environments, Evolutionary Computation 8(4), pp. 393-418.
• [17] Lin, L. (1993). Reinforcement Learning for Robots Using Neural Networks, Technical Report CMU-CS-93-103, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
• [18] Mtivier, M., Lattaud, C. (2002). Anticipatory Classifier System using Behavioral Sequences in non-Markov Environments, In Proceedings of IWLCS2002, pages 143-162, Springer-Verlag.
• [19] Russell, S., Norvig, P. (2003). Artificial Intelligence: A Modern Approach, Second Edition, Prentice Hall Series in Artificial Intelligence. Englewood Cliffs, New Jersey.
• [20] Stolzmann, W. (1998). Anticipatory Classifier Systems, In Proceedings of the Third Annual Genetic Programming Conference, pages 658-664.Morgan Kaufmann.
• [21] Tomlinson, A., Bull, L. (1998). A Corporate Classifier System, In Proceedings of the Fifth International Conference on Parallel Problem Solving from Nature - PPSN V, number 1498 in LNCS, pp. 550-559, Springer-Verlag.
• [22] Tomlinson, A., Bull, L. (1999). A Zeroth Level Corporate Classifier System, In Proceedings of the Genetic and Evolutionary Computation Conference Workshop Program, pp. 306-313, Morgan Kaufmann: San Francisco CA.
• [23] Tomlinson, A., Bull, L. (1999). On Corporate Classifier Systems: Increasing the Benefits of Rule Linkage, In Proceedings of the Genetic and Evolutionary Computation Conference, pp. 549-656,Morgan Kaufmann: San Francisco CA.
• [24] Tomlinson, A., Bull, L. (2002). An Accuracy Based Corporate Classifier System, Soft Computing. 6(3-4): 200-215.
• [25] Widrow, B., Hoff, M. (1960). Adaptive Switching Circuits, In Western Electronic Show and Convention, Volume 4, pages 96-104, Institute of Radio Engineers.
• [26] Wilson, S.W. (1994). ZCS: a Zeroth Level Classifier System, Evolutionary Computation, 1(2):1-18.
• [27] Wilson, S.W. (1995). Classifier Fitness Based on Accuracy, Evolutionary Computation 3(2):149-175.
• [28] Zatuchna, Z. (2005). AgentP: a Learning Classifier System with Associative Perception in Maze Environments, PhD. Thesis, University of East Anglia.