Abstract
This paper describes how a group of agents can anticipate the possible changes within a virtual environment, than this concept the actors explore and adapts to the environment, thanks to mechanisms of evolution and adaptation, that provide the genetic algorithms and the classifier systems, with this capacity to learning and take one action by resolve a problem or change his objectifies. The main idea of the anticipation is based on the acquisition of knowledge of events happened in the environment of the past, later to compare them with the actions that appear in the present, in a time specify. The events of the past to like learning and make the calculus and the interpolating with the actions that happen in the present, this way to be able to make all the calculations necessary to be able to anticipate the possible changes in the environment [Hoffmann, J (1993)].
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References
Butz, M.V., Sigaud, O., Gerard, P.: Internal Models and Anticipations in Adaptive Learning Systems, Department of Cognitive Psychology, University of Würzburg, Germany
Isla, D., Burke, R., Downie, M.: Bruce Blumberg. A Layer Brain Architecture for Synthetic Creatures. The Media Laboratory Massachusetts Institute of Technology, USA
Anticipation and Cognition. Hogrefe (1993)
Holland, J.H.: Adaptation in Natural and Artificial Systems, pp. 171–198, MIT press edition
The Origins of Order: Self-Organization and Selection in Evolution, Oxford University
Artificial life, Addison-Wesley
Learning classifier system from reinforcement perspective. Soft Computing, 6(3-4), 162–170
Reynolds, C.: Flocks, Herds, and Schools: A Distributed Behavioural Model. Computer Graphics (SIGGRAPH 1987 Conference Proceedings) 21(4), 25–34 (1987)
Sanchez, S., Duthen, H.L.Y., Balet, O.: Virtual Behavior: A framework for real-time animation of animation of autonomous characters. In: International Symposium and School on Advanced Distributed Systems 2002 (ISSADS), Guadalajara, Jal., México (2003)
Stolzmann, W.: Anticipatory classifier systems, Genetic programming 1998. In: Proceedings of the third Annual Conference, San Francisco, CA, pp. 658–664 (1998)
Stolzmann, W.: Introducing a genetic generalization pressure to the anticipatory classifier system: Part 1 theoretical approach. In: Proceeding of the Genetic and evolutionary Computation Conference (GECCO 2000), San Francisco, CA, pp. 34–41.
Veloso, M., Stone, P., Bowling, M.: Anticipation: A Key For Collaboration in Team of Agents. Computer Science Department. Carnegie-Mellon University
Learning whit delayed rewards. Doctoral dissertation, Psychology department, University of Cambridge, England
Wilson, S.W.: Classifier Fitness Based on Accuracy Evolutionary Computation 3(2) 149-175
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Ramos, M.A., Berro, A., Duthen, Y. (2006). Anticipative Emergence in Environment Virtual. In: Böhme, T., Larios Rosillo, V.M., Unger, H., Unger, H. (eds) Innovative Internet Community Systems. IICS 2004. Lecture Notes in Computer Science, vol 3473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553762_13
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DOI: https://doi.org/10.1007/11553762_13
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