Do the right thing: studies in limited rationality
Do the right thing: studies in limited rationality
Practical Issues in Temporal Difference Learning
Machine Learning
A Bayesian approach to relevance in game playing
Artificial Intelligence - Special issue on relevance
Actor-Critic--Type Learning Algorithms for Markov Decision Processes
SIAM Journal on Control and Optimization
Temporal difference learning for heuristic search and game playing
Information Sciences: an International Journal - Special issue on Heuristic search and computer game playing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving Neural Networks to Play Go
Applied Intelligence
Explanation-Based Generalization: A Unifying View
Machine Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Some studies in machine learning using the game of checkers
IBM Journal of Research and Development
Incorporating opponent models into adversary search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Move Ordering Using Neural Networks
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
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The strength of a game-playing program is mainly based on the adequacy of the evaluation function and the efficacy of the search algorithm. This paper investigates how temporal difference learning and genetic algorithms can be used to improve various decisions made during game-tree search. The existent TD algorithms are not directly suitable for learning search decisions. Therefore we propose a modified update rule that uses the TD error of the evaluation function to shorten the lag between two rewards. The genetic algorithms can be applied directly to learn search decisions. For our experiments we selected the problem of time allocation from the set of search decisions. On each move the player can decide on a certain search depth, being constrained by the amount of time left. As testing ground, we used the game of Lines of Action, which has roughly the same complexity as Othello. From the results we conclude that both the TD and the genetic approach lead to good results when compared to the existent time-allocation techniques. Finally, a brief discussion of the issues that can emerge when the algorithms are applied to more complex search decisions is given.