Preference Learning for Move Prediction and Evaluation Function Approximation in Othello

This paper investigates the use of preference learning as an approach to move prediction and evaluation function approximation, using the game of Othello as a test domain. Using the same sets of features, we compare our approach with least squares temporal difference learning, direct classification,...

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Veröffentlicht in:IEEE transactions on computational intelligence and AI in games. Jg. 6; H. 3; S. 300 - 313
Hauptverfasser: Runarsson, Thomas Philip, Lucas, Simon M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2014
Schlagworte:
Approximation
Boards
Computational and artificial intelligence
Games
Learning
Least squares method
Mathematical analysis
Mathematical models
Monte Carlo methods
n-tuple
Othello
preference learning
Radiation detectors
Selectors
temporal difference learning
Training
Trajectory
Vectors
ISSN:1943-068X, 1943-0698
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Zusammenfassung:This paper investigates the use of preference learning as an approach to move prediction and evaluation function approximation, using the game of Othello as a test domain. Using the same sets of features, we compare our approach with least squares temporal difference learning, direct classification, and with the Bradley-Terry model, fitted using minorization-maximization (MM). The results show that the exact way in which preference learning is applied is critical to achieving high performance. Best results were obtained using a combination of board inversion and pair-wise preference learning. This combination significantly outperformed the others under test, both in terms of move prediction accuracy, and in the level of play achieved when using the learned evaluation function as a move selector during game play.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1943-068X
1943-0698
DOI:10.1109/TCIAIG.2014.2307272