Del 62 i serien Studies in Fuzziness and Soft Computing

Computational Intelligence in Games

Inbunden, Engelska, 2001

1 389 kr

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The recent advances in computational intelligence paradigms have generated tremendous interest among researchers in the theory and implementation of games. Game theory involves the mathematical calculations and heuristics to optimize the efficient lines of play. This book presents the main constituents of computational intelligence paradigms including knowledge representation, probability-based approaches, fuzzy logic, neural networks, genetic algorithms, and rough sets. It includes a new approach of evolving a neural network to play checkers without human expertise. The book will be useful to researchers and practitioners who are interested in developing game techniques in computational intelligence environment.

Produktinformation

Utgivningsdatum2001-02-27
Mått155 x 235 x 15 mm
Vikt436 g
FormatInbunden
SpråkEngelska
SerieStudies in Fuzziness and Soft Computing
Antal sidor161
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783790813487

Tillhör följande kategorier

1 Introduction to computational intelligence paradigms.- 1 Introduction.- 2 Search and knowledge representation.- 3 Probability-based approaches.- 4 Fuzzy logic.- 5 Artificial neural networks.- 6 Genetic algorithms.- 7 Rough sets.- 8 Fusion.- 9 Discussion and conclusion.- References.- 2 Evolving a neural network to play checkers without human expertise.- 1 Introduction.- 2 Background on neural networks and evolutionary computation.- 3 Background on computer programs for checkers.- 4 Method and results of evolving neural networks for checkers from scratch.- 5 Discussion.- References.- Appendix: Moves for Game 1 — Anaconda vs. “Beatrice” from Hoyle’s Classic Games.- 3 Retrograde analysis of patterns versus metaprogramming.- 1 Introduction.- 2 The game of Hex and the game of Go.- 3 Retrograde analysis of patterns.- 4 Metaprogramming.- 5 Using the generated knowledge.- 6 Future work and conclusion.- References.- 4 Learning to evaluate Go positions via temporal difference methods.- 1 Introduction.- 2 Temporal difference learning.- 3 Network architecture.- 4 Training strategies.- 5 Empirical results.- 6 Summary.- References.- 5 Model-based reinforcement learning for evolving soccer strategies.- 1 Introduction.- 2 The soccer simulator.- 3 RL with CMAC models.- 4 PIPE.- 5 Experiments.- 6 Conclusion.- References.- Appendix A: Prioritized sweeping.- Appendix B: Non-pessimistic value functions.- Appendix C: Q(?)-learning.- 6 Fuzzy rule-based strategy for a market selection game.- 1 Introduction.- 2 Market selection game.- 3 Game strategies.- 4 Fuzzy rule-based strategy.- 4.1 Fuzzy Q-learning.- 4.2 Simulation results.- 4.3 Knowledge acquisition.- 5 Conclusions.- References.- List of Contributors.