Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Experiments in search and knowledge
Experiments in search and knowledge
Low overhead alternatives to SSS*
Artificial Intelligence
Principles of artificial intelligence
Principles of artificial intelligence
Conspiracy numbers for min-max search
Artificial Intelligence
The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
Minimax Search Algorithms With and Without Aspiration Windows
IEEE Transactions on Pattern Analysis and Machine Intelligence
A world championship caliber checkers program
Artificial Intelligence
Artificial Intelligence
Temporal difference learning and TD-Gammon
Communications of the ACM
Best-first fixed-depth minimax algorithms
Artificial Intelligence
Enhanced Iterative-Deepening Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of speedup in distributed algorithms
Analysis of speedup in distributed algorithms
Information acquisition in minimal window search
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
How to use limited memory in heuristic search
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Best-first fixed-depth game-tree search in practice
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Transposition table driven work scheduling in distributed search
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Performance Analysis of Transposition-Table-Driven Work Scheduling in Distributed Search
IEEE Transactions on Parallel and Distributed Systems
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Learning to play Go using recursive neural networks
Neural Networks
GIB: imperfect information in a computationally challenging game
Journal of Artificial Intelligence Research
GIB: steps toward an expert-level bridge-playing program
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Minimum proof graphs and fastest-cut-first search heuristics
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Hi-index | 0.00 |
The state space of most adversary games is a directed graph. However, due to the success of simple recursive algorithms based on Alpha-Beta, theoreticians and practitioners have concentrated on the traversal of trees, giving the field the name "game-tree search." This paper shows that the focus on trees has obscured some important properties of the underlying graphs. One of the hallmarks of the field of game-tree search has been the notion of the minimal tree, the smallest tree that has to be searched by any algorithm to find the minimax value. In fact, for most games it is a directed graph. As demonstrated in chess and checkers, we show that the minimal graph is significantly smaller than previously thought. proving that there is more room for improvement of current algorithms. We exploit the graph properties of the search space to reduce the size of trees built in practice by at least 25%. For over a decade, fixed-depth Alpha-Beta searching has been considered a closed subject, with research moving on to more application-dependent techniques. This work opens up new avenues of research for further application-independent improvements.