Computer algorithms for game playing rely on a state evaluation which is based on a set of features and patterns. Such evaluation can, however, never fully capture the full complexity of games such as chess, since the impact of a feature or a pattern on the game outcome heavily relies on the game's context. It is a well-known problem in pattern-based learning that too many too specialized patterns are needed to capture all possible situations. We hypothesize that a pattern should be regarded as an opportunity to attain a certain state during the continuation of the game, which we call the effect of a pattern. For correct game state evaluation, one should analyze whether the desired effects of the matched patterns can be reached. Patterns indicate opportunities to reach a more advantageous situation. Testing whether this is possible in the current context is performed through a well-directed game tree exploration. We argue that this approach comes closer to the human way of game playing.
Lemeire, J 2008, An Alternative Approach for Playing Complex Games like Chess. in Annual machine learning conference of Belgium and The Netherlands. Annual machine learning conference of Belgium and The Netherlands, Finds and Results from the Swedish Cyprus Expedition: A Gender Perspective at the Medelhavsmuseet, Stockholm, Sweden, 21/09/09. <http://parallel.vub.ac.be/~jan>
Lemeire, J. (2008). An Alternative Approach for Playing Complex Games like Chess. In Annual machine learning conference of Belgium and The Netherlands (Annual machine learning conference of Belgium and The Netherlands). http://parallel.vub.ac.be/~jan
@inproceedings{acacb311a73b4c6f89fb514b5230cd66,
title = "An Alternative Approach for Playing Complex Games like Chess",
abstract = "Computer algorithms for game playing rely on a state evaluation which is based on a set of features and patterns. Such evaluation can, however, never fully capture the full complexity of games such as chess, since the impact of a feature or a pattern on the game outcome heavily relies on the game's context. It is a well-known problem in pattern-based learning that too many too specialized patterns are needed to capture all possible situations. We hypothesize that a pattern should be regarded as an opportunity to attain a certain state during the continuation of the game, which we call the effect of a pattern. For correct game state evaluation, one should analyze whether the desired effects of the matched patterns can be reached. Patterns indicate opportunities to reach a more advantageous situation. Testing whether this is possible in the current context is performed through a well-directed game tree exploration. We argue that this approach comes closer to the human way of game playing.",
keywords = "game playing",
author = "Jan Lemeire",
year = "2008",
month = may,
day = "22",
language = "English",
series = "Annual machine learning conference of Belgium and The Netherlands",
booktitle = "Annual machine learning conference of Belgium and The Netherlands",
note = "Finds and Results from the Swedish Cyprus Expedition: A Gender Perspective at the Medelhavsmuseet ; Conference date: 21-09-2009 Through 25-09-2009",
}