A world championship caliber checkers program
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Temporal difference learning and TD-Gammon
Communications of the ACM
New advances in Alpha-Beta searching
CSC '96 Proceedings of the 1996 ACM 24th annual conference on Computer science
KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Artificial Intelligence for Games (The Morgan Kaufmann Series in Interactive 3D Technology)
Artificial Intelligence for Games (The Morgan Kaufmann Series in Interactive 3D Technology)
Using aspiration windows for minimax algorithms
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Temporal difference learning applied to a high-performance game-playing program
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Some studies in machine learning using the game of checkers
IBM Journal of Research and Development
| Hi-index | 0.00 |
The NeuroDraughts is a good automatic draughts player which uses temporal difference learning to adjust the weights of an artificial neural network whose role is to estimate how much the board state represented in its input layer by NET-FEATUREMAP is favorable to the player agent. The set of features is manually defined. The search for the best action corresponding to a current state board is performed by minimax algorithm. This paper presents new and very successful results obtained by substituting an efficient tree-search module based on alpha-beta pruning, transposition table and iterative deepening for the minimax algorithm in NeuroDraughts. The runtime required for training the new player was drastically reduced and its performance was significantly improved.