Automated Identification of Patterns in Evaluation Functions

Kaneko, Tomoyuki; Yamaguchi, Kazunori; Kawai, Shinya

doi:10.1007/978-0-387-35706-5_18

Cited by 5 publications

(3 citation statements)

References 15 publications

(27 reference statements)

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“…The evaluation function is easy to analyze and optimize by hand because we can check each evaluation feature individually. This group includes ZENITH [12], ELF [13], GLEM [2], Kaneko's method [14], and Duminy's method [15].…”

Section: Related Workmentioning

confidence: 99%

Automatic Generation of Evaluation Features for Computer Game Players

Misawa

Yokoyama

Chikayama

2007

2007 IEEE Symposium on Computational Intelligence and Games

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Abstract-Accuracy of evaluation functions is one of the critical factors in computer game players. Evaluation functions are usually constructed manually as a weighted linear combination of evaluation features that characterize game positions. Selecting evaluation features and tuning their weights require deep knowledge of the game and largely alleviates such efforts.In this paper, we propose a new fast and scalable method to automatically generate game position features based on game records to be used in evaluation functions. Our method treats two-class problems which is widely applicable to many types of games. Evaluation features are built as conjunctions of the simplest features representing positions. We select these features based on two measures: frequency and conditional mutual information.To evaluate the proposed method, we applied it to 200,000 Othello positions. The proposed selection method is found to be effective, showing much better results than when simple features are used. The Naïve Bayesian classifier using automatically generated features showed the accuracy close to 80% in win/lose classification. We also show that this generation method can be parallelized easily and can treat large scale problems by converting these selection algorithms into incremental selection algorithms.

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Section: Related Workmentioning

confidence: 99%

Automatic Generation of Evaluation Features for Computer Game Players

Misawa

Yokoyama

Chikayama

2007

2007 IEEE Symposium on Computational Intelligence and Games

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“…Kaneko, Yamaguchi, and Kawai [19] automatically generated and sieved through patterns, given only the logical specifications of Othello and a set of training positions. The resulting evaluation function was comparable in accuracy (though not speed of execution) to that developed by Buro [8].…”

Section: Feature Definition and Selectionmentioning

confidence: 99%

Tuning evaluation functions by maximizing concordance

Gomboc

Buro

Marsland

2005

Theoretical Computer Science

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Heuristic search effectiveness depends directly upon the quality of heuristic evaluations of states in a search space. Given the large amount of research effort devoted to computer chess throughout the past half-century, insufficient attention has been paid to the issue of determining if a proposed change to an evaluation function is beneficial. We argue that the mapping of an evaluation function from chess positions to heuristic values is of ordinal, but not interval, scale. We identify a robust metric suitable for assessing the quality of an evaluation function, and present a novel method for computing this metric efficiently. Finally, we apply an empirical gradient ascent procedure, also of our design, over this metric to optimize feature weights for the evaluation function of a computer chess program. Our experiments demonstrate that evaluation function weights tuned in this manner give equivalent performance to hand-tuned weights.

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“…Document [7] [8] introduces a method for automatic construction of evaluation function. Technical report [9] proposed a way to adjust parameters of chess valuation function by using automated identification of patterns. At present, the method of automatic adjustment of parameters is an important trend, which can greatly shorten the design time of the evaluation function and reduce the manual work [10] .…”

Section: The Introduction Of Intelligent Algorithms In Computer Gamementioning

confidence: 99%

An improved algorithm model based on machine learning

Zhou

Huan

et al. 2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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In the last decades, Reinforcement Learning (RL) algorithm has attracted more and more attention, and become the research focus in the field of machine learning. This paper leads the typical RL algorithm, Q-learning algorithm, into computer game platform (Connect6), and proposes an improved method. We adjust reward parameter according to the shape of Connect6, and optimize the adjustment of evaluation function to achieve the global optimization. Moreover, the optimization of the reward makes the valueless units away from the evaluation, to reduce the interference of valueless units for optimal results and improve the convergence speed, thereby reducing the overall time of self-learning process.

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Automated Identification of Patterns in Evaluation Functions

Cited by 5 publications

References 15 publications

Automatic Generation of Evaluation Features for Computer Game Players

Automatic Generation of Evaluation Features for Computer Game Players

Tuning evaluation functions by maximizing concordance

An improved algorithm model based on machine learning

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