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Ayo, the Awari player, or how better representation trumps deeper search

Awari is a two-player end-game played on a plank with 12 pits and 48 seeds; the goal of the game is to collect 25 seeds before the other player does. In this paper, we illustrate the importance of problem domain representation, using our own Awari playing program, Ayo. We use a genetic algorithm to optimize the weights of the feature evaluation function of Ayo. We play Ayo against a commercially available Awari player, then compare Ayo's results to those achieved by an older Awari player; one that uses a 7-level deep minimax search. Ayo, with a 5-level deep minimax search, returns better results, due to better, more intelligent, representation of the state space.