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RESEARCH PRODUCT

On Applying Adaptive Data Structures to Multi-Player Game Playing

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In the field of game playing, the focus has been on two-player games, such as Chess and Go, rather than on multi-player games, with dominant multi-player techniques largely being an extension of two-player techniques to an \(N\)-player environment. To address the problem of multiple opponents, we propose the merging of two previously unrelated fields, namely those of multi-player game playing and Adaptive Data Structures (ADS). We present here a novel move-ordering heuristic for a dominant multi-player game playing algorithm, namely the Best-Reply Search (BRS). Our enhancement uses an ADS to rank the opponents in terms of their respective threat levels to the player modeled by the AI algorithm. This heuristic, referred to as Threat-ADS, has been rigorously tested, and the results conclusively demonstrate that, while it cannot damage the performance of BRS, it performs better in all cases examined.