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Knowledge-Based Search in Competitive Domains
May/June 2003 (vol. 15 no. 3)
pp. 734-743

Abstract—Artificial Intelligence programs operating in competitive domains typically use brute-force search if the domain can be modeled using a search tree or alternately use nonsearch heuristics as in production rule-based expert systems. While brute-force techniques have recently proven to be a viable method for modeling domains with smaller search spaces, such as checkers and chess, the same techniques cannot succeed in more complex domains, such as shogi or go. This research uses a cognitive-based modeling strategy to develop a heuristic search technique based on cognitive thought processes with minimal domain specific knowledge. The cognitive-based search technique provides a significant reduction in search space complexity and, furthermore, enables the search paradigms to be extended to domains that are not typically thought of as search domains such as aerial combat or corporate takeovers.

[1] G.M. Adelson-Velsky, V.L. Arlazarov, and M.V. Donsky, Algorithms for Games. New York: Springer-Verlag, 1988.
[2] I. Althöfer, “Data Compression Using an Intelligent Generator: The Storage of Chess Games as an Example,” Artificial Intelligence, vol. 52, no. 1, pp. 109-113, 1991.
[3] V. Anand, “More Questions Than Answers,” may11story_3.html, 1997.
[4] J.R. Anderson, Cognitive Psychology and Its Implications. New York: Freeman, second ed., 1985.
[5] A. Barr and E.A. Feigenbaum, The Handbook of Artificial Intelligence. vol. 1,Reading, Mass.: Addison-Wesley, 1981.
[6] H. Berliner and C. Ebeling, “Pattern Knowledge and Search: The SUPREM Architecture,” Technical Report CMU-CS-88-109, Carnegie-Mellon Univ., Pittsburgh, Penn., 1988.
[7] H. Berliner and G. Goetsch, “A Quantitative Study of Search Methods and the Effect of Constraint Satisfaction,” Technical Report CMU-CS-84-147, Carnegie-Mellon Univ., Pittsburgh, Penn., 1984.
[8] D.A. Blum, “Kasparov Versus Deep Blue: The Showdown Between Human and Computer,” PC Artificial Intelligence, vol. 15, no. 1, pp. 49-51, 2001.
[9] I. Bratko and D. Michie, “An Advice Program for a Complex Chess Programming Task,” The Computer J., vol. 23, no. 4, pp. 353-359, 1980.
[10] M.S. Campbell, “Chunking as an Abstraction Mechanism,” PhD thesis, Carnegie-Mellon Univ., Pittsburgh, Penn., 1988.
[11] N. Charness, “Expertise in Chess: The Balance Between Knowledge and Search,” Toward A General Theory of Expertise, K.A. Ericsson and J. Smith, eds., pp. 39-63, 1991.
[12] W.G. Chase and H.A. Simon, “Perception in Chess,” Cognitive Psychology, vol. 4, no. 1, pp. 55-81, 1973.
[13] N.J. Cooke, “Modeling Human Expertise in Expert Systems,” The Psychology of Expertise, Cognitive Research and Empirical Artificial Intelligence, R. Hoffman, ed., pp. 29-60, 1992.
[14] K.A. DeJong and A.C. Schultz, “Using Experience-Based Learning in Game Playing,” Proc. Fifth Int'l Conf. Machine Learning, pp. 284-290, 1988.
[15] A. Elithorn and R. Banerji, Artificial and Human Intelligence. New York: Elsevier, 1984.
[16] S.L. Epstein, J. Gelfand, and J. Lesniak, “Pattern-Based Learning and Spatially Oriented Concept Formation in a Multi-Agent, Decision-Making Expert,” Computational Intelligence, vol. 12, no. 1, pp. 199-221, 1996.
[17] K.A. Ericsson and J. Smith, “Prospects and Limits of the Empirical Study of Expertise: An Introduction,” Toward A General Theory of Expertise, K.A. Ericsson and J. Smith, eds., pp. 1-38, 1991.
[18] K.A. Ericsson and J. Staszewski, “Skilled Memory and Expertise: Mechanisms of Exceptional Performance,” Complex Information Processing The Impact of Herbert A. Simon, D. Klahr and K. Kotovsky, eds., pp. 235-267, 1989.
[19] P.W. Frey, Chess Skill in Man and Machine. New York: Springer-Verlag, 1983.
[20] R. Grimbergen, “Using Pattern Recognition and Selective Deepening to Solve Tsume Shogi,” Proc. Game Programming Workshop in Japan '96, pp. 150-159, 1996.
[21] A. Horowitz, The World Chess Championship A History. New York: Macmillan, 1973.
[22] H. Iida, J.W.H.M. Uiterwijk, H.J. Van den Herik, and I.S. Herschberg, “Potential Applications of Opponent-Model Search, Part 2: Risks and Strategies,” Int'l Computer Chess Assoc. J., vol. 17, no. 1, pp. 10-14, 1994.
[23] H. Iida, J.W.H.M. Uiterwijk, H.J. Van den Herik, and I.S. Herschberg, “Thoughts on the Application of Opponent-Model Search,” Advances in Computer Chess, D.F. Beal, ed., vol. 7, pp. 61-78, 1995.
[24] J. Laird, P. Rosenbloom, and A. Newell, Universal Subgoaling and Chunking. Norwell, Mass: Kluwer Academic, 1986.
[25] K. Lee and S. Mahajan, “A Pattern Classification Approach to Evaluation Function Learning,” Artificial Intelligence, vol. 36, no. 1, pp. 1-25, 1988.
[26] D.N. Levy, “Chess Master Versus Computer,” Advances in Computer Chess, D.F. Beal, ed., vol. 4, pp. 181-194, 1986.
[27] H. Matsubara, H. Iida, and R. Grimbergen, “Natural Developments in Game Research: From Chess to Shogi to Go,” Int'l Computer Chess Assoc. J., vol. 19, no. 2, pp. 103-112, 1996.
[28] M. Newborn, Kasparov Versus Deep Blue Computer Chess Comes of Age. New York: Springer, 1997.
[29] M. Newborn, “History at the Chess Table,” htmle.8.5.html, 1997.
[30] M. Newborn and D. Kopec, “The Twentieth Annual ACM North American Computer Chess Championship,” Comm. ACM, vol. 33, pp. 92-104, 1990.
[31] J.R. Quinlan, “Learning Efficient Classification Procedures and Their Application to Chess End Games,” Machine Learning An Artificial Intelligence Approach, R.S. Michalski, ed., pp. 463-482, 1983.
[32] A.L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers,” IBM J. Research and Development, vol. 3, no. 3, pp. 210-229, 1959.
[33] A.L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers,” IBM J. Research and Development, vol. 11, no. 6, pp. 601-617, 1967.
[34] H.A. Simon and K. Gilmartin, “A Simulation of Memory for Chess Positions,” Cognitive Psychology, vol. 5, pp. 29-46, 1973.
[35] S. Walczak, “Using Inductive Inference of Past Performance to Build Strategic Cognitive Adversary Models,” PhD thesis, Univ. of Florida, Gainesville, Fla., 1990.
[36] S. Walczak, “Pattern-Based Tactical Planning,” Int'l J. Pattern Recognition and Artificial Intelligence, vol. 6, no. 5, pp. 955-988, 1992.
[37] S. Walczak and D.D. Dankel, “Acquiring Tactical and Strategic Knowledge With a Generalized Method for Chunking of Game Pieces,” Int'l J. Intelligent Systems, vol. 8, no. 2, pp. 249-270, 1993.
[38] S. Walczak and P. Fishwick, “A Quantitative Analysis of Pattern Production and Its Relationship to Expert Performance,” J. Experimental and Theoretical Artificial Intelligence, vol. 9, no. 1, pp. 83-101, 1997.
[39] S. Walczak and R. Grimbergen, “Pattern Analysis and Analogy in Shogi: Predicting Shogi Moves from Prior Experience,” Knowledge and Information Systems, vol. 2, no. 2, pp. 185-200, 2000.
[40] D.A. Waterman, “Generalization Learning Techniques for Automating the Learning of Heuristics,” Artificial Intelligence, vol. 1, nos. 1-2, pp. 121-170, 1970.
[41] S. Webb, Chess for Tigers. New York: Pergamon Press, 1986.
[42] J.F. White, “The Amateur's Book-Opening Routine,” Int'l Computer Chess Assoc. J., vol. 13, no. 1, pp. 22-26, 1990.
[43] B. Wilcox, “Reflections on Building Two Go Programs,” SIGART Newsletter, vol. 94, pp. 29-43, 1985.
[44] D.E. Wilkins, “Using Patterns and Plans in Chess,” Artificial Intelligence, vol. 14, no. 2, pp. 165-203, 1980.
[45] D.E. Wilkins, “Using Knowledge to Control Tree Searching,” Artificial Intelligence, vol. 18, no. 1, pp. 1-51, 1982.
[46] F.C. Zagare, Game Theory Concepts and Applications. Newbury Park, Calif.: Sage Publications, 1984.

Index Terms:
Search, knowledge-based, chunking, opponent modeling, heuristic pruning, chess.
Steven Walczak, "Knowledge-Based Search in Competitive Domains," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 3, pp. 734-743, May-June 2003, doi:10.1109/TKDE.2003.1198402
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