Abstract
Models of adaptive behaviour are normally highly complex due to a greater emphasis on, learning by interaction with the environment, flexible behaviour, and, an evolutionary learning methodology. This research investigates how a combination of Genetic Algorithms and Neural Networks (GA+NN) can be used to model behaviour to solve a task requiring co-operation between two artificial autonomous agents. In particular, the results illustrate how a complex learning task (that is, a simple game of football which requires a highly dynamic interaction between agents and the environment) is learned more efficiently as a result of GA operator modifications and modularisation of the learning task. The overall effect of these changes is to constrain the search space that the hybrid GA+NN system can potentially explore. A distinction between task and search complexity provides a useful framework for a clearer comprehension of the nature of constraints vis-a-vis the modelling process (generally characterised as a complex adaptive system). Broad implications of the findings on modular models of adaptive behaviour and the role of constraints on complexity are briefly discussed.
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References
Beer, R. D., Gallagher, J. C.: Evolving dynamical neural networks for adaptive behavior. Adaptive behavior. 1(1) 91–122 (1992)
Beer. R.: A Dynamical Systems Perspective on Autonomous Agents. Case Western Reserve University Technical Report CES-92-11. Cleveland, Ohio. (1992)
Belew, R. K., McInerney, J., Schraudolph, N. N.: Evolving networks: Using genetic algorithms with connectionist learning. CSE technical report CS90-174, University of California at San Diego, CA. (1990)
Bersini, H., Seront, G.: In search of a good crossover between evolution and optimization. Parallel Problem Solving from Nature 2, B. Manderick, R. Manner (eds.) Amsterdam: Elsevier 479–488. (1992)
Davis, L. (ed.): Handbook of genetic algorithms. NY: Van Nostrand Reinhold (1991)
Dorigo, M.: ALECSYS and the AutonoMouse: Learning to Control a Real Robot by Distributed Classifier Systems. Machine Learning 19 (3) 209–240 (1995)
Dorigo, M., Colombetti M.: Robot Shaping: Developing Autonomous Agents through Learning. Artificial Intelligence 71 (2) 321–370 (1995)
Goldberg, D. E.: Genetic algorithms in search, optimisation and machine learning. Reading, MA:Addison-Wesley (1989)
Holland, J. H.: Adaption in natural and artificial systems. The University of Michigan Press, Ann Arbor, MI. (1975)
Koza, J. R.: Genetic Programming: A paradigm of genetically breeding computer population of computer programs to solve problems. Cambridge, MA: MIT Press (1992)
Kuscu, I., Thornton, C. Design of Artificial Neural Network Using Genetic Algorithms: review and prospect. Cognitive and Computing School Technical Report, University of Sussex, Falmer, England. (1994)
Langton, C., Taylor, C., Farmer, J., Rasmussen, S. (eds.): Proceedings of Artificial Life II. Santa Fe Institute Studies in the Sciences of Complexity, Vol 10. Redwood City, CA: Addison-Wesley (1991)
Meyer, J. A., Roitblat, H. L., Wilson, W. (eds.): From Animals to Animats. Proceedings of the Second International Conference on Simulation of Adaptive Behaviour. Cambridge, Mass: MIT Press. (1991)
Nicolis G., Prigogine I.: Exploring Complexity: An Introduction (1989)
Nolfi, S., Parisi, D.: Evolving Artificial Neural Networks that Develop in Time. In Advances in Artificial Life: Proceeding of ECAL 95, F. Moran, A. Moreno, J. J. Merelo and P. Chacon (eds.) Lecture Notes in Artificial Intelligence 929 Berlin: Springer-Verlag 353–367 (1995)
Patel, M. J.: Concept Formation: A Complex Adaptive Approach. Theoria, No 20 89–108, Universidad del Pais Vasco, San Sabastian, Spain. ISSN 0495-548 (1994a)
Patel, M. J.: Situation Assessment and Adaptive Learning: Theoretical and Experimental issues. In proceedings of the Second International Round-Table on Abstract Intelligent, Agent, Rome: ENEA Headquarters. And as ERCIM Research Report, ERCIM-03/94-R030 CNR, Rocquencourt, France (1994b)
Patel, M. J.: Advance Tutorial ou Hybrid Systems of Genetic Algorithms and Neural Networks (GA+NN). Mss copy available from author or anonymous ftp at ftp.cs.iastate.edu (pub/gann/papers/patel_tutorial_94.ps.Z) (1994c)
Patel, M. J.: An evaluation of the nature of representations in complex adaptive (learning) systems. Journal of Cognitive Systems, forthcoming (1995)
Patel, M. J., Schnepf, U.: Concept Formation as Emergent Phenomena. In Proceedings of the first, European Conference on Artificial Life, F. J. Varela, P. Bourgine (eds.), Cambridge, MA: MIT Press (1991)
Patel, M. J., Maniezzo, V.: NN's and GA's: Evolving co-operative behaviour in adaptive learning agents. In proceedings of the IEEE World Congress on Computational Intelligence: Evolutionary Computation, Orlando, Florida: IEEE Computers. (1994)
Patel, M. J., Dorigo, M.: Adaptive Learning of a Robot. In Selected papers from AISB Workshop on Evolutionary Computation (Leeds, UK), T. C. Fogerty (ed.). Lecture Notes in Computer Science 865, 180–194 Berlin: Springer-Verlag (1994)
Patel, M. J., Dorigo, M., Colombetti M.: Evolutionary Learning for Intelligent Automation: A Case Study. Journal of Intelligent, Automation and Soft Computing, 1 (1) 29–42 (1995)
Schaffer, J. D. Whitley, D. Eshelman, L. J.: Combinations of Genetic Algorithms and Neural Networks: A Survey of the State of the Art. Proc. Int. wks. on Combinations of Genetic Algorithms and Neural Networks (COGANN-92), (1992) 1–37.
van Gelder, T.: What might cognition be if not computation? University of Indianna Technical Report 75 (1992)
Yao, X.: A Review of Evolutionary Artificial Neural Networks. Tech. Rep. Commonwealth Scientific and Industrial Research Organization, Victoria, Australia (1992)
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Patel, M.J. (1995). Constraints on task and search complexity in GA+NN models of learning and adaptive behaviour. In: Fogarty, T.C. (eds) Evolutionary Computing. AISB EC 1995. Lecture Notes in Computer Science, vol 993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60469-3_36
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DOI: https://doi.org/10.1007/3-540-60469-3_36
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