Abstract
An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert’s knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. Co-evolutionary fitness functions are discussed. The strategy tree concept and its relationship to co-evolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. Co-evolutionary data mining alters the geometric properties of the overlap region known as the combined admissible region of phase space significantly enhancing the performance of the resource manager. Significant experimental results are provided.
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References
Blackman, S., Popoli, R.: Design and Analysis of Modern Tracking Systems, ch. 11. Artech House, Boston (1999)
Tsoukalas, L.H., Uhrig, R.E.: Fuzzy and Neural Approaches in Engineering, ch. 5. John Wiley and Sons, New York (1997)
Bigus, J.P.: Data Mining with Neural Nets, ch. 1. McGraw-Hill, New York (1996)
Holland, J.H.: Hidden Order How Adaptation Builds Complexity, pp. 1–15. Perseus Books, Reading (1995)
Zimmerman, H.J.: Fuzzy Set Theory and its Applications, ch. 1. Kluwer Academic Publishers Group, Boston (1991)
Smith III, J.F., Rhyne II, R.: A Resource Manager for Distributed Resources: Fuzzy Decision Trees and Genetic Optimization. In: Arabnia, H. (ed.) Proceeding of the International Conference on Artificial Intelligence, IC-AI 1999, pp. 669–675. CSREA Press, Las Vegas (1999)
Smith III, J.F., Rhyne II, R.: Fuzzy Logic Resource Management and Coevolutionary Game-Based Optimization. NRL/FR/5741–01-10001, Naval Research Laboratory, Washington D.C. 20375-5000 (September 2001)
Smith III, J.F., Rhyne II, R.: Methods of Automated Rule Discovery in a Multi-agent Adversarial Distributed Environment. In: Smari, W. (ed.) Proceeding of the International Symposium on Information Systems and Engineering, ISE 2001, pp. 25–31. CSREA Press (2001)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Cliff, D., Miller, G.F.: Co-evolution of Pursuit and Evasion II: Simulation Methods and Results. In: Maes, P., Mataric, M., Meyer, J.-A., Pollack, J., Wilson, S.W. (eds.) Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior (SAB 1996), pp. 1–10. MIT Press Bradford Books, Cambridge (1996)
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Smith, J.F. (2004). Automating Co-evolutionary Data Mining. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_69
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DOI: https://doi.org/10.1007/978-3-540-28651-6_69
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