Computer Game-Playing Bibliography

Michael P. Frank MIT Lab for Computer Science Cambridge, Massachusetts

March 30, 1994

References

[1] L. V. Allis, H. J. van den Herik, and M. P. H. Huntjens. Go-moku solved by new search techniques. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[2] American Association for Artificial Intelligence. Games: Planning and Learning, Papers from the 1993 Fall Symposium, Raleigh, North Carolina, October 22–24, 1993. AAAI Press, Menlo Park, California. Technical Report FS9302.

[3] Christopher G. Atkeson. Memory-based approaches to learning to play games. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[4] Eric B. Baum. How a bayesian approaches games like chess. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[5] Jean R. S. Blair, David Mutchler, and Cheng Liu. Games with imperfect information. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[6] David Carmel and Shaul Markovitch. Learning models of opponent’s strategy in game playing. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[7] Susan L. Epstein. Toward a theory for well-guided search. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[8] Susan L. Epstein, Jack Gelfand, Joanna Lesniak, and Pascal Abadie. The integration of visual cues into a multiple-advisor game-learning program. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[9] Steven Gordon. A comparison between probabilistic search and weighted heuristics in a game with incomplete information. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[10] Michael R. Hieb, David Hille, and Gheorghe Tecuci. Designing a computer opponent for wargames: Integrating planning, knowledge acquisition and learning in WARGLES. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[11] Richard E. Korf. Best-first minimax search: First results. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[12] Masao Kubo and Yukinori Kakazu. Learning team plays in a competition for foods between ant colonies as autonomous agents. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[13] Robert Levinson. Exploiting the physics of state-space search. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[14] David H. Lorenz and Shaul Markovitch. Derivative evaluation function learning using genetic operators. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[15] Stan Melax. New approaches to moving target search. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[16] Barney Pell. A strategic metagame player for general chess-like games. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[17] Jonathan Schaeffer, Paul Lu, Duane Szafron, and Robert Lake. A reexamination of brute-force search. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[18] Stephen J. J. Smith and Dana S. Nau. Strategic planning for imperfectinformation games. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[19] Stephen J. J. Smith and Dana S. Nau. Toward an analysis of forward pruning. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[20] Gerald Tesauro. TD-gammon, a self-teaching backgammon program, achieves master-level play. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[21] Michael van Lent and David Mutchler. A pruning algorithm for imperfect information games. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].

[22] Jiajie Zhang. The interaction between perceptual and cognitive processes in a distributed problem solving task. In Games: Planning and Learning, Papers from the 1993 Fall Symposium [2].