Study and improvement of search algorithms in two-players perfect information games
This work addresses a gap in evaluating search algorithm generality for two-player zero-sum games, which is incremental as it builds on existing methods with specific performance improvements.
The authors tackled the lack of studies evaluating the generality of search algorithms in two-player perfect information games by proposing a new algorithm that outperforms existing ones in experiments, achieving superior performance on all games for short search times and on 17 out of 22 games for medium search times.
Games, in their mathematical sense, are everywhere (game industries, economics, defense, education, chemistry, biology, ...).Search algorithms in games are artificial intelligence methods for playing such games. Unfortunately, there is no study on these algorithms that evaluates the generality of their performance. We propose to address this gap in the case of two-player zero-sum games with perfect information. Furthermore, we propose a new search algorithm and we show that, for a short search time, it outperforms all studied algorithms on all games in this large experiment and that, for a medium search time, it outperforms all studied algorithms on 17 of the 22 studied games.