CHAPTER 7 BIBLIOGRAPHY

Transcription

1 CHAPTER 7 BIBLIOGRAPHY 1. Ian Millington and John Funge, Artificial Intelligence for Games, Second Edition, 2. De Voogt A, A classification of board games, New Approaches to Board Game Research : Asian origins and Future Perspectives 3. Cameron Browne, Automatic Generation and Evaluation of Recombination Games, Ph D thesis E de Groote, Machine learning in Go- Supervised learning of move prediction, Thesis March T. Srinivasan, P.J.S. Srikanth, K. Praveen and L. Harish Subramaniam, AI Game Playing Approach for Fast Processor Allocation in Hypercube Systems using Veitch diagram (AIPA), IADIS International Conference on Applied Computing 2005, vol. 1, Feb. 2005, pp Brian Rose, Othello A minute to learn A life time to master A book registered to Anjar Co, Julian Togelius, Optimization, Imitation and Innovation: Computational Intelligence and Games, Ph D Thesis September Raph Koster, A theory of fun for game design, Paraglyph press, Thomas W. Malone, What makes things fun to learn? heuristics for designing instructional computer games, In Proceedings of the 3rd ACM SIGSMALL symposium and the first SIGPC symposium on Small systems, pages , Monroe Newborn, Kasparov Vs. Deep Blue: Computer Chess Comes of Age, Springer, 1997.

2 Bibliography Jonathan Schaeffer, Joseph Culberson, Norman Treloar, Brent Knight, Paul Lu, and Duane Szafron, A world championship caliber checkers program, Artificial Intelligence, 53(2 3): , Michael Buro, The evolution of strong othello programs, In Entertainment Computing - Technology and Applications, pages Kluwer, David E. Moriarty and Risto Miikkulainen, Discovering complex Othello strategies through evolutionary neural networks, Connection Science, 7(2): , Michael Buro, Improving heuristic mini-max search by supervised learning 15. Thomas Robert Lincke, Exploring the Computational Limits of Large Exhaustive Search Problems, Ph D Thesis, Jing Yang, Simon Liao, and Mirek Pawlak, A Decomposition Method for Finding Solution in Game Hex 7x7, International Conference On Application and Development of Computer Games in the 21st Century, pages , Jing Yang, Simon Liao, and Mirek Pawlak, A New Solution for 7x7 Hex Game to appear, L. Victor Allis, Searching for Solutions in Games and Artificial Intelligence, Ph D thesis, University of Limburg, Maastricht, The Netherlands, Marco Kunze, Sebastian Nowozin, An AI for Gomoku/Wuziqi and more Erik van der Werf, AI techniques for the game of GO Ph D thesis, January E. A. Heinz, Scalable Search in Computer Chess, Vieweg Verlag, Braunschweig, Germany, D. J. C. MacKay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, Cambridge, UK, A. Junghanns and J. Schaeffer, Search versus knowledge in game-playing programs revisited, In Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97), pages Morgan Kaufmann, San Francisco, CA, J. Schaeffer, Experiments in Search and Knowledge, Ph.D. thesis, Department of Computing Science, University of Waterloo, Waterloo, Canada, Stuart Russel & Peter Norvig, Artificial Intelligence A modern Approach Second Edition

3 Bibliography Andrea Schalk, AI and Games Part 1: The Theory of Games, September Fogel, D., Hays, T., Hahn, S., Quon, J, A self-learning evolutionary chess program, Proceedings of the IEEE 92 (2004) 27. Holland, J. H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence, Ann Arbor, MI: University of Michigan Press. 28. Goldberg, D. E, Genetic Algorithms in Search, Optimization and Machine Learning Reading, MA: Addison-Wesley. 29. Randy L Haupt & Sue Ellen Haupt, Practical Genetic Algorithms, Second Edition, John Willey & Sons 30. Dorigo M and G. Maria, Ant colony system: a cooperative learning approach to the traveling salesman problem, IEEE Trans. Evolv. Comput. 1: Holland, J. H, Genetic Algorithms Sci. Am. 267: Man, K. F., Tang, K. S., Kwong, S., and Halang, W. A, Genetic Algorithms: Concepts and Designs Springer-Verlag. 33. Mitchell, Melanie. An Introduction to Genetic Algorithms MA: The MIT Press 34. Stender, Joachim, Hillebrand, and Kingdon, J. Genetic Algorithms in Optimization, Simulation, and Modeling, Amsterdam: IOS Publishing. 35. Coley, David A., An introduction to Genetic Algorithms for Scientists and Engineers, Singapore: World Scientific 36. Kennedy J and R. C. Eberhart, Swarm Intelligence, San Fransisco: Moragan Kaufmann 37. Kennedy J and R. C Eberhart, Particle Swarm Optimization, Proc. IEEE Int. Conf. on Neural networks IV, NJ: IEEE Service center, pp Rosenbloom, P, A world championship level Othello program, Artificial Intelligence, 19: Billman, D., and Shaman, D, Strategy knowledge and strategy change in skilled performance: A study of the game Othello, American Journal of Psychology, 103: Koza, John R. Genetic Programming: On the Programming of Computers by Means of Natural Selection, Cambridge, MA: The MIT Press.

4 Bibliography Langdon, William B. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! Amsterdam: Kluwer Academic Publishers. 42. Radha Shankarmani, Saurabh Jain & Gaurang Sinha, Game Architecture and Programming,Wiley India Pvt. Ltd., website Buyya, Selvi & Chu, Object Oriented Programming with Java essentials and applications, McGrawHill 45. Blaha & Rambuagh, Object oriented modeling & Design with UML Pearson, Second Edition 46. Scott Oaks & Henry Wong, Java Threads, O reilly 47. Ralph Morelli & Ralph Walde, Java, Java, Java : Object-Oriented Problem Solving, Prentice Hall 48. Ting Qian, Using Genetic Algorithm to Solve Sliding Tile Puzzles 49. Thomas Weise., Global Optimization Algorithms Theory and Application, Jorg Heitkotter and David Beasley, Hitch-Hiker s Guide to Evolutionary Computation: A List of Frequently Asked Questions (FAQ), ENCORE (The EvolutioNary Computation REpository Network), USENET 51. Agostinho Rosa, Solving Puzzles and Games by Evolutionary Algorithms, Carlos Artemio and Ceollo Coello, A comprehensive survey of evolutionary based multiobjective optimization techniques, Knowledge and Information Systems, 1(3): , August Carlos Artemio and Ceollo Coello, An updated survey of evolutionary multiobjective optimization techniques: State of the art and future trends, In 1999 Congress on Evolutionary Computation, pages 3 13, Darse Billings, Algorithms and Assessment in Computer Poker, Ph D Thesis, Robert Abrey Hearn, Games, Puzzles and Computation, Ph D Thesis, 2006

5 Bibliography Sanaz Mostaghim, Multi-objective Evolutionary Algorithms: Data structures, Convergence and, Diversity PhD thesis, Deutschland (Germany), Sharker Verlag, Todd Blackman, Genetic Algorithm for a Multiagent Approach to the Game of GO, Master of Science Thesis, Warren D Smith, Mathematical Definition of Intelligence (and Consequences), Franz Rothlauf, Representations for Genetic and Evolutionary Algorithms. Physica-Verlag, second edition, August 2002 (1st ed.), 2006 (2nd ed.) 60. Eric Krevice Prebys, Genetic Algorithm in Computer Science 61. T. Hashimoto, Y. Kajihara, N. Sasaki, H. Iida, J. Yoshimura, An evaluation function for amazons,h.j. van den Herik, B.Monien (Eds.), Advances in Computer Games, Vol. 9, Universiteit Maastricht, Maastricht, 2001, pp M. Winands, J. Uiterwijk, J. van den Herik, The quad heuristic in Lines of Action, J. Internat. Computer Chess Assoc. 24 (1) (2001) J. Schaeffer, J. Culberson, N. Treloar, B. Knight, P. Lu, D. Szafron, Reviving the game of checkers, D.N.L. Levy, D.F. Beal (Eds.), Heuristic Programming in Artificial Intelligence 2: The Second Computer Olympiad, Ellis Horwood, Chichester, 1991, pp J. Schaeffer, R. Lake, Solving the game of checkers, in: R.J. Nowakowski (Ed.), Games of No Chance, MSRI Publications, Vol. 29, Cambridge University Press, Cambridge, MA, 1996, pp J. Allen, A note on the computer solution of Connect-Four, D.N.L. Levy, D.F. Beal (Eds.), Heuristic Programming in Artificial Intelligence: The First Computer Olympiad, Ellis Horwood, Chichester, 1989, pp L.V. Allis, A knowledge-based approach of Connect Four: The game is over, white to move wins, M.Sc. Thesis, Vrije Universiteit Report No. IR-163, Faculty of Mathematics and Computer Science, Vrije Universiteit, Amsterdam, 1988.

6 Bibliography J. Schaeffer, Conspiracy numbers, in: D.F. Beal (Ed.), Advances in Computer Chess, Vol. 5, Elsevier Science, Amsterdam, 1989, pp Also published in: Artificial Intelligence 43 (1) (1990) L.V. Allis, P.N.A. Schoo, Qubic solved again, in: H.J. van den Herik, L.V. Allis (Eds.), Heuristic Programming in Artificial Intelligence 3: The Third Computer Olympiad, Ellis Horwood, Chichester, 1992, pp J. Wágner, I. Virág, Solving Renju, ICGA J. 24 (1) (2001) H.J. van den Herik, I.S. Herschberg, The construction of an omniscient endgame data base, ICCA J. 8 (2) (1985) P.J. Jansen, Using knowledge about the opponent in game-tree search, Ph.D. Thesis, School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, D.M. Breuker, J.W.H.M. Uiterwijk, H.J. van den Herik, Replacement schemes for transposition tables, ICCA J. 17 (4) (1994) T. Thomsen, Lambda-search in game trees With application to Go, ICGA J. 23 (4) (2000) L.V. Allis, M. van der Meulen, H.J. van den Herik, Proof-number search, Artificial Intelligence 66 (1) (1994) M. Seo, H. Iida, J.W.H.M. Uiterwijk, The PN*- search algorithm: Application to Tsume-Shogi, Artificial Intelligence 129 (1 2) (2001) J. van Rijswijck, Computer Hex: Are bees better than fruitflies?, M.Sc. Thesis, University of Alberta, Edmonton, AB, 2000.