Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Searching with probabilities
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
The SUPREM architecture: a new intelligent paradigm
Artificial Intelligence
Advances in computer chess
Low overhead alternatives to SSS*
Artificial Intelligence
Artificial intelligence
Artificial intelligence (2nd ed.)
Artificial intelligence (2nd ed.)
Principles of artificial intelligence
Principles of artificial intelligence
Generalization of alpha-beta SSS* search procedures
Artificial Intelligence
Game tree searching by min/max approximation
Artificial Intelligence
New Hitech computer chess success
AI Magazine
Chess-playing programs and the problem of complexity
Computers & thought
A parallel search chess program
ACM '85 Proceedings of the 1985 ACM annual conference on The range of computing : mid-80's perspective: mid-80's perspective
Experiments With Some Programs That Search Game Trees
Journal of the ACM (JACM)
Decision Quality As a Function of Search Depth on Game Trees
Journal of the ACM (JACM)
Improved parallel alpha-beta search
ACM '86 Proceedings of 1986 ACM Fall joint computer conference
Parallel Search of Strongly Ordered Game Trees
ACM Computing Surveys (CSUR)
The solution for the branching factor of the alpha-beta pruning algorithm and its optimality
Communications of the ACM
Experiments with the M & N tree-searching program
Communications of the ACM
Computer Game - Playing: Theory and Practice
Computer Game - Playing: Theory and Practice
Chess and Computers
Computer Chess
Solving Inexact Search Problems
Solving Inexact Search Problems
Computers, Chess and Long-Range Planning
Computers, Chess and Long-Range Planning
A new polynomial-time algorithm for linear programming
STOC '84 Proceedings of the sixteenth annual ACM symposium on Theory of computing
The Alpha-Beta Heuristic
Recursive random games: a probabilistic model for perfect information games
Recursive random games: a probabilistic model for perfect information games
The expected-outcome model of two-player games
The expected-outcome model of two-player games
Expected-Outcome: A General Model of Static Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A bibliography on minimax trees
ACM SIGACT News
Search Heuristics for Box Decomposition Methods
Journal of Global Optimization
Increasing assignment motivation using a game Al tournament
Proceedings of the 8th annual conference on Innovation and technology in computer science education
Playing around in the CS curriculum: reversi as a teaching tool
Journal of Computing Sciences in Colleges
Adversarial Search by Evolutionary Computation
Evolutionary Computation
When is it better not to look ahead?
Artificial Intelligence
A move generating algorithm for hex solvers
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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Computer games have been around for almost as long as computers. Most of these games, however, have been designed in a rather ad hoc manner because many of their basic components have never been adequately defined. In this paper some deficiencies in the standard model of computer games, the minimax model, are pointed out and the issues that a general theory must address are outlined. Most of the discussion is done in the context of control strategies, or sets of criteria for move selection. A survey of control strategies brings together results from two fields: implementations of real games and theoretical predictions derived on simplified game-trees. The interplay between these results suggests a series of open problems that have arisen during the course of both analytic experimentation and practical experience as the basis for a formal theory.