The learning of evaluation functions from game records has been widely studied in the field of computer game-playing. Conventional learning methods optimize the evaluation function parameters by using the game records of expert players in order to imitate their plays. Such conventional methods utilize objective functions to increase the agreement between the moves selected by game-playing programs and the moves in the records of actual games. The methods, however, have a problem in that increasing the agreement does not always improve the strength of a program. Indeed, it is not clear how this agreement relates to the strength of a trained program. To address this problem, this paper presents a learning method to optimize objective function parameters for strength in game-playing. The proposed method employs an evolutionary learning algorithm with the strengths (Elo ratings) of programs as their fitness scores. Experimental results show that the proposed method is effective since programs using the objective function produced by the proposed method are superior to those using conventional objective functions.

This paper presents a method of evaluating game tree search methods including standard min-max search with heuristic evaluation functions and Monte Carlo tree search, which recently achieved drastic improvements in the strength of Computer Go programs. The basic idea of this paper is to use an averaged win probability of positions having similar evaluation values. Accuracy measures of evaluation values with respect to win probabilities can be used to assess the performance of game tree search methods. A plot of win probabilities against evaluation values should have consistency and monotonicity if the evaluation values are produced by a good game tree search method. By inspecting whether the plot has the properties for some subset of positions, we can detect specific deficiencies in the game tree search method. We applied our method to Go, Shogi, and Chess, and by comparing the results with empirical understanding of the performance of various game tree search methods and with the results of self-plays, we show that our method is efficient and effective.