Kasparov Vs. Deep Blue: Computer Chess Comes of Age
Kasparov Vs. Deep Blue: Computer Chess Comes of Age
Using upper confidence bounds for online learning
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Automatic feature extraction for autonomous general game playing agents
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Automatic heuristic construction in a complete general game player
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Heuristic evaluation functions for general game playing
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Fluxplayer: a successful general game player
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
CI in general game playing: to date achievements and perspectives
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Multigame playing by means of UCT enhanced with automatically generated evaluation functions
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
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General Game Playing (GGP) is a specially designed environment for creating and testing competitive agents which can play variety of games. The fundamental motivation is to advance the development of various artificial intelligence methods operating together in a previously unknown environment. This approach extrapolates better on real world problems and follows artificial intelligence paradigms better than dedicated single-game optimized solutions. This paper presents a universal method of constructing the heuristic evaluation function for any game playable within the GGP framework. The algorithm embraces distinctive discovery of candidate features to be included in the evaluation function and learning their correlations with actions performed by the players and the game score. Our method integrates well with the UCT algorithm which is currently the state-of-the-art approach in GGP.