Gherrity, M. (1993). A Game-Learning Machine. Ph. D. thesis, University of California, San Diego, CA. Home/Search Document Details and Download
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Machine Learning In Computer Chess: The Next Generation - Fürnkranz (1996) (1 citation) (Correct)
....and is a user settable parameter (0 1) If is set to 1, all positions in the game are weighted equally, while with = 0 (Q learning) only the current position evaluation is used for adjustment. In that case the evaluation function is trained with its own value one move later in the game. Gherrity (1993) has integrated Q learning with consistency search (Beal 1980) in the SAL ( Search and Learning ) system. A neural network evaluation function 4 is trained with self play, where the evaluation function of the next board position is used as the target value for the evaluation value of the current ....
Gherrity, M. (1993). A Game-Learning Machine. Ph. D. thesis, University of California, San Diego, CA.
Learning To Play the Game of Chess - Thrun (1995) (27 citations) (Correct)
....TD Gammon approach plays grandmaster level backgammon, recent attempts to reproduce these results in the context of Go [12] and chess have been less successful. For example, Schafer [11] reports a system just like Tesauro s TD Gammon, applied to learning to play certain chess endgames. Gherrity [6] presented a similar system which he applied to entire chess games. Both approaches learn purely inductively from the final outcome of games. Tadepalli [15] applied a lazy version of explanation based learning [5, 7] to endgames in chess. His approach learns from the final outcome, too, but unlike ....
....fl (with 0 fl 1) is a so called discount factor. It decays V exponentially in time and hence favors early over late success. Notice that in NeuroChess V is represented by an artificial neural network, which is trained to fit the target values V target obtained via Eqs. 1) and (2) cf. [6, 11, 12, 16]) 3 Explanation Based Neural Network Learning In a domain as complex as chess, pure inductive learning techniques, such as neural network Back Propagation, suffer from enormous training times. To illustrate why, consider the situation of a knight fork, in which the opponent s knight attacks our ....
Michael Gherrity. A Game-Learning Machine. PhD thesis, University of California, San Diego, 1993.
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