Learning Classifier Systems : a way of reinforcement learning based on evolutionary techniques

Wasielewska, K.; Seredyński, M.

Artykuł - szczegóły

Tytuł artykułu

Learning Classifier Systems : a way of reinforcement learning based on evolutionary techniques

Autorzy

Wasielewska K. , Seredyński M.

Identyfikatory

Warianty tytułu

Konferencja

Evolutionary Computation and Global Optimization 2006 / National Conference (9 ; 31.05-2.06.2006 ; Murzasichle, Poland)

Języki publikacji

Abstrakty

Learning classifier systems (LCSs) are rule-based learning machines in which a reinforcement learning is conducted with use of evolutionary techniques. Currently, they are a subject of intensive study and of interesting applications. In this paper we present a review of LCSs. We give a short history of LCSs and overview current models. We also present some interesting and successful applications of LCSs

Słowa kluczowe

Learning Classifier Systems LCS machine learning reinforcement learning evolutionary computation

systemy uczące się wzmocnione uczenie obliczenia ewolucyjne

Wydawca

Oficyna Wydawnicza Politechniki Warszawskiej

Czasopismo

Prace Naukowe Politechniki Warszawskiej. Elektronika

Rocznik

2006

Tom

z. 156

Strony

385--395

Opis fizyczny

Bibliogr. 84 poz., rys.

Twórcy

autor

Wasielewska K.

autor

Seredyński M.

University of Applied Science and Art in Elblag, Institute of Applied Informatics, Elblag, Poland, kawa@pwsz.elblag.pl

Bibliografia

[1] Afanasyeva H. Fuzzy Learning Classifier Systems for Classification Task. Transport and Telecommunication, Vol. 3, No. 3, 43-51, 2002.
[2] Bacardit J., Garrell J.M. Bloat control and generalization pressure using the minimum description length principle for a Pittsburgh approach learning classifier system. In Proceedings of the 6th International Workshop on Learning Classifier Systems, LNAI, Springer, 2003.
[3] Bagnall A.J., Cawley G.C. Learning classifier systems for data mining: A comparison of XCS with other classifiers for the Forest Cover dataset. In Proceedings of the IJCNN, Portland, Oregon, USA, Vol. 3, 1802-1807, 2003.
[4] Barry A.M. Aliasing in XCS and the Consecutive State Problem 1 - Problems. In Banzhaf W. et al. (editors), In Proceedings of the GECCO. Morgan Kaufmann, 19-26, 1999.
[5] Barry A.M. Aliasing in XCS and the Consecutive State Problem 2 - Solutions. In Banzhaf W. et al. (editors), In Proceedings of the GECCO. Morgan Kaufmann, 27-34, 1999.
[6] Barry A.M., Holmes J., Llorà X. Data Mining using Learning Classifier Systems. In Bull L. (editor), Applications of Learning Classifier Systems, Springer-Verlag, 2004.
[7] Bonarini A. An Introduction to Learning Fuzzy Classifier Systems. In Lanzi P.L., Stolzmann W., Wilson S.W. (editors), Learning Classifier Systems. From Foundations to Applications, LNAI 1813, Berlin, Springer-Verlag, 83-104, 2000.
[8] Bonelli P., Parodi A., Sen S., Wilson S.W. NEWBOOLE: A Fast GBML System. ML, 153-159, 1990.
[9] Booker L.B. Intelligent Behavior as an Adaptation to the Task Environment. Ph.D. Dissertation, University of Michigan, 1982.
[10] Booker L.B. Triggered Rule Discovery in Classifier Systems. Proceedings of the ICGA, 265-274, 1989.
[11] Bull L., Sha'Aban J., Tomlinson A., Addison J.D., Heydecker B.G. Towards distributed adaptive control for road traffic junction signals using learning classifier systems. In Bull L. (editor), Applications of learning classifier systems. Studies in Fuzziness and Soft Computing (150), Springer, New York, 279-299, 2004.
[12] Butz M.V., Goldberg D.E., Stolzmann W. Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System Part 1: Theoretical Approach. In Proceedings of the GECCO, 34-41, 2000.
[13] Butz M.V., Goldberg D.E., Stolzmann W. Introducing a Genetic Generalization Pressure to the Anticipatory Classifier System Part 2: Performance Analysis. In Proceedings of the GECCO, 42-49, 2000.
[14] Butz M.V. Anticipatory learning classifier systems, Boston, MA: Kluwer Akademic Publishers, 2002.
[15] Butz M.V., Goldberg D.E. Generalized state values in an anticipatory learning classifier system. In Butz M.V., Sigaud O., Gérard P. (editors) Anticipatory Behavior in Adaptive Learning Systems, Springer-Verlag, Berlin Heidelberg, 282-301, 2002.
[16] Butz M.V., Sastry K., Goldberg D.E. Tournament Selection in XCS. In Proceedings of the GECCO, Springer Verlag, Berlin, 1857-1869, 2003.
[17] Carse B., Pipe A.G. X-FCS: a fuzzy classifier systems using accuracy based fitness - first results. In Proceedings of the EUSFLAT, 195-198, 2001.
[18] Cao Y.J., Ireson N., Bull L., Miles R. An Evolutionary Intelligent Agents Approach to Traffic Signal Control. International Journal of Knowledge-based Intelligent Engineering Systems 5(4), 279-289, 2001.
[19] DeJong K.A., Spears W.M., Gordon D.F. Using genetic algorithms for concept learning. Machine Learning, 13(2/3), 161-188, 1993.
[20] Donnart J.Y., Meyer J.A. Hierarchical-map building and self-positioning with MonaLysa. Adaptive Behavior, 5(1), 29-74, 1996.
[21] Dorigo M., Sirtori E. Alecsys: A Parallel Laboratory for Learning Classifier Systems. In Proceedings of 4th International Conference on Genetic Algorithms, San Diego, California, Morgan Kaufmann, San Mateo, CA, 1991.
[22] Dorigo M. Genetic and Non-Genetic Operators in Alecsys. Evolutionary Computation, 1, 2, MIT Press, 151-164, 1993.
[23] Dorigo M., Colombetti M. Robot Shaping: An Experiment in Behaviour Engineering. MIT Press/Bradford Books 1997. In Special issue on Complete Agent Learning in Complex Environments, M.J. Mataric (Ed.), Adaptive Behavior, 5, 3-4, 391-406, 1997.
[24] Federman F., Dorchak S.F. Representation of Music in a Learning Classifier System. ISMIS, 267-276, 1997.
[25] Frey P.W., Slate D.J. Letter recognition using Holland-style adaptive classifiers. Machine Learning, 6, 161-182, 1991.
[26] Gérard P., Stolzmann W., Sigaud O. YACS: a new Learning Classifier System using Anticipation. Journal of Soft Computing, Special Issue on Learning Classifier Systems, Berlin, Springer-Verlag, 2000.
[27] Gérard P., Sigaud O. Designing efficient exploration with macs: Modules and function approximation. In Proceedings of the GECCO, Chicago, IL, Springer-Verlag, 1882-1893, 2003.
[28] Giordana A., Saitta L. Regal: an integrated system for learning relations using genetic algorithms. In Proceedings of 2nd International Workshop on Multistrategy Learning, Morgan Kaufmann, 234-249, 1993.
[29] Goldberg D.E. Computer-Aided Gas Pipeline Operation Using Genetic Algorithms and Rule Learning. Ph.D. Dissertation, University of Michigan, 1993.
[30] Goldberg D.E., (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Co. 1989.
[31] Guessoum Z., Rejeb L., Sigaud O. Using XCS to build adaptive agents. Proceedings of the AAMAS Symposium, Leeds, 2004.
[32] Harik G., Lobo F., Goldberg D.E. The compact genetic algorithm. In Proceedings of the IEEE International Conference on Evolutionary Computation, 523-528, 1998.
[33] Holland J.H. Processing and processors for schemata. In Associative Information Techniques. Jacks E.L. (editor) Elsevier, 127-46, 1971.
[34] Holland J.H., Reitman J. Cognitive systems based on adaptive algorithms. In Waterman D., Hayess-Roth F. (editors), Pattern-directed Inference Systems. Academic Press, New York, 1978.
[35] Holmes J.H. Evolution-Assisted Discovery of Sentinel Features in Epidemiologic Surveillance. PhD thesis, Drexel University, 1996.
[36] Holmes J.H. Learning Classifier Systems Applied to Knowledge Discovery in Clinical Research Databases. Learning Classifier Systems, 243-262, 1999.
[37] Holmes J.H., Sager J.A. Rule Discovery in Epidemiologie Surveillance Data Using EpiXCS: An Evolutionary Computation Approach. AIME 2005, 444-452, 2005.
[38] Ishibuchi H., Nakashima T., Murata T. Performance Evaluation of Fuzzy Classifier Systems for Multi-Dimensional Pattern Classification Problems. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 29, No. 5, 601-618, 1999.
[39] Katagami D., Yamada S. Interactive Classifier System for Real Robot Learning. IEEE International Workshop on Robot and Human Interaction (ROMAN), 258-263, 2000.
[40] Katagami D., Yamada S. Real Robot Learning with Human Teaching, The 4th Japan-Australia Joint Workshop on Intelligent and Evolutionary Systems, 263-270, 2001.
[41] Katagami D., Yamada S. Interactive Evolutionary Computation for Real Robot from Viewpoint of Observation. The 7th International Conference on Intelligent Autonomous, 158-165, 2002.
[42] Lanzi P.L. Adding Memory to XCS. In Proceedings of the IEEE World Congress on Computational Intelligence. The 1998 IEEE International Conference on Evolutionary Computation, May 4-9 Anchorage (AL), IEEE Press, 609-614, 1998.
[43] Lanzi P.L. An analysis of the memory mechanism of XCSM. In Koza J.R., Banzhaf W., Chellapilla K., Deb K., Dorigo M., Fogel D.B., Garzon M.H., Goldberg D.E., Iba H., Riolo R. (editors), In Proceedings of the 3rd Annual Conference, San Francisco, CA, USA, Morgan Kaufmann, 643-651, 1998.
[44] Lanzi P.L., Wilson S.W. Optimal classifier system performance in non-Markov environments. Technical Report 99.36, Dipartimento di Elettronica e Informazione, Politecnico di Milano, 1999.
[45] Lanzi P.L. Extending the Representation of Classifier Conditions Part I: From Binary to Messy Coding. In Banzhaf W., Daida J., Eiben A.E., Garzon M.H., Honavar V., Jakiela M., Smith R.E. (editors), In Proceedings of the GECCO, Orlando (FL), Morgan Kaufmann, 337-344, 1999.
[46] Lanzi P.L., Perrucci A. Extending the Representation of Classifier Conditions Part II: From Messy Coding to S-Expressions. In Banzhaf W., Daida J., Eiben A.E., Garzon M.H., Honavar V., Jakiela M., Smith R.E. (editors), In Proceedings of the GECCO, Orlando (FL), Morgan Kaufmann, 345-352, 1999.
[47] Lanzi P.L., Riolo R.L. A Roadmap to the Last Decade of Learning Classifier System Research. In Lanzi P.L., Stolzmann W., Wilson S.W. (editors). Learning Classifier Systems. From Foundations to Applications, LNAI 1813, Springer, 33-62, 2000.
[48] Lanzi P.L., Loiacono D., Wilson S.W., Goldberg D.E. XCS with computed prediction in continuous multistep environments. IlliGAL Report, 2005.
[49] Llorà X., Garrell J.M. Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In Proceedings of the 3rd Genetic and Evolutionary Computation Conference, Morgan Kaufmann, 461-468, 2001.
[50] Llorà X. Genetic Based Machine Learning using Fine-grained Parallelism for Data Mining. PhD thesis, Enginyeria i Arquitectura La Salle. Ramon Llull University, Barcelona, 2002.
[51] Llorà X., Goldberg D.E., Traus I., Bernadó E., Mansilla I. Accuracy, Parsimony and Generality in Evolutionary Learning Systems via Multiobjective Selection. IWLCS, 118-142, 2002.
[52] Llorà X., Sastry K., Goldberg D.E. The compact classifier system: Scalability analysis and first results. In Proceedings of the Congress on Evolutionary Computation, 1, 596-603, 2005.
[53] Richards R.A. Zeroth-order shape optimization utilizing a learning classifier system. Web version of book: http://www.stanford.edu/~buc/SPHINcsX/book.html, 1995.
[54] Riolo R.L. CFS-C: A Package of Domain-Independent Subroutines for Implementing Classifier Systems in Arbitrary User-Defined Environments. Technical Report, University of Michigan, 1988.
[55] Roberts G.R. Dynamic planning for Classifier Systems. Forrest S. (editor), Proceedings of the 5th International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, 231-237, 1993.
[56] Sanza C., Panatier C., Duthen Y. Communication and Interaction with Learning Agents in Virtual Soccer. 2nd International Conference on Virtual Worlds. LNCS 1834 Juillet, Paris, FRANCE, 2000.
[57] Sato Y., Kanno R. Event-driven learning classifier systems for online soccer games. GECCO, 2201-2202, 2005.
[58] Sen S., Sekaran M. Multi-agent coordination with learning classifier systems. Proceedings of the IJCAI Workshop on Adaption and Learning in Multi-Agent Systems, Weiß G., Sen S. (editors), Vol. 1042, Springer Verlag, 218-233, 1996.
[59] Sha'Aban J., Tomlinson A., Heydecker B.G., Bull L. Adaptive Traffic Control Using Evolutionary Algorithms. In Proceedings of 9th Meeting of the EURO Working Group on Transportation, Bari, Italy, 2002.
[60] Smith R.E. Memory Exploitation in Learning Classifier Systems. Evolutionary Computation 2(3), 199-220, 1994.
[61] Smith R.E., Dike B.A., El-Fallah A., Mehra R.K. The Fighter Aircraft LCS: A Case of Different LCS Goals and Techniques. In Lanzi P.L., Stolzmann W., Wilson S.W. (editors), Learning Classifier Systems: From Foundations to Applications, Springer, 283-300, 2000.
[62] Stolzmann W. Antizipative Classifier Systems. Ph.D. Thesis, Fachbereich Mathematik/Informatik, University of Osnabrueck, 1997.
[63] Swartz R., Using a Classifier System to Parse English Text, Princeton University, web version: http://www.cs.princeton.edu/~rswartz/classifier/writeup.html
[64] Tharakunnel K., Goldberg D.E. XCS with Average Reward Criterion in Multi-Step Environment. IlliGAL Technical Report 2002008, 2002.
[65] Tolman E.C. Purposive Behaviour in Animals and Man, New York, Appleton, 1932.
[66] Tomlinson A., Bull L. A Corporate Classifier System. In Eiben A.E., Baeck T., Schoenauer M., Schwefel H.P. (editors), Parallel Problem Solving from Nature - PPSN V. Springer Verlag, 550-559, 1998.
[67] Tomlinson A., Bull L. A Corporate XCS. In Lanzi P.L., Stolzmann W., Wilson S.W., editors, Learning Classifier Systems: From Foundations to Applications, Springer, 194-208, 2000.
[68] Unold O., Dabrowski G. Use of Learning Classifier System for Inferring Natural Language Grammar. HIS, 272-278, 2003.
[69] Valenzuela-Rendón M. The Fuzzy Classifier System: A Classifier System for Continuously Varying Variables. ICGA, 346-353, 1991.
[70] Vargas P.A., Lyra C., Von Zuben F.J. Application of Learning Classifier Systems to the On Line Reconfiguration of Electric Power Distribution Networks. In Bull L. (editor), Applications of Learning Classifier Systems, Springer Series: Studies in Fuzziness and Soft Computing, Vol. 150, 260-275, 2004.
[71] Vasilyev A. Autonomous Agent Control Using Connectionist XCS Classifier System. Transport and Telecommunication, Vol. 3, No 3, 56-63, 2002.
[72] Watkins C.J.C.H. Learning from Delayed Rewards. Ph.D thesis, Cambridge University, 1989.
[73] Wilson S.W. Classifier Systems and the Animat Problem. Machine Learning 2: 219-228, 1987.
[74] Wilson S.W., Goldberg, D.E. A critical review of classifier systems. In Proceedings of the Third International Conference on Genetic Algorithms, Los Altos, California: Morgan Kaufmann, 244-255, 1989.
[75] Wilson S.W. ZCS: A Zeroth-level Classifier System. Evolutionary Computation 2(1), 1-18, 1994.
[76] Wilson S.W. Classifier Fitness Based on Accuracy. Evolutionary Computation, 3(2), 149-76, 1995.
[77] Wilson S.W. Generalization in the XCS classifier system. In Proceedings of the Third Annual Conference, 665-614, 1998.
[78] Wilson S.W. Get real! XCS with continuous-valued inputs. In Lanzi P.L., Stolzmann W., Wilson S.W. (Eds.), Learning classifier systems: From foundations to applications, LNAI 1813, Berlin Heidelberg: Springer-Verlag, 209-219, 2000.
[79] Wilson S.W. Function approximation with a classifier system. In L.S. et al., editor, In Proceedings of GECCO, San Francisco, California, USA, Morgan Kaufmann, 974-981, 2001.
[80] Wilson S.W. Mining oblique data with XCS. In Lanzi, P.L., Stolzmann, W., & Wilson, S.W. (Eds.), Advances in learning classifier systems: Third international workshop, IWLCS 2000, LNAI 1996, Berlin Heidelberg: Springer-Verlag, 158-174, 2001.
[81] Wilson S.W. Classifiers that approximate functions. Journal of Natural Computating, 1(2-3), 211-234, 2002.
[82] Wilson S.W. Classifier systems for continuous payoff environments. In K.D. et al., editor, GECCO, Part II, LNCS 3103, Springer-Verlag, 824-835, 2004.
[83] Cliff D., Ross S. Adding Temporary Memory to ZCS. Adaptive Behavior, 3(2), 101-150, 1994.
[84] Dam H.H., Abbass H.A., Lokan Ch. DXCS: an XCS System For Distributed Data Mining. In Proceedings of GECCO, 2005.

Typ dokumentu

Bibliografia

Identyfikator YADDA

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