Distance Features for General Game Playing Agents

General Game Playing (GGP) is concerned with the development of programs that are able to play previously unknown games well. The main problem such a player is faced with is to come up with a good heuristic evaluation function automatically. Part of these heuristics are distance measures used to estimate, e.g., the distance of a pawn towards the promotion rank. However, current distance heuristics in GGP are based on too specific detection patterns as well as expensive internal simulations, they are limited to the scope of totally ordered domains and/or they apply a uniform Manhattan distance heuristics regardless of the move pattern of the object involved. In this paper we describe a method to automatically construct distance measures by analyzing the game rules. The presented method is an improvement to all previously presented distance estimation methods, because it is not limited to specific structures, such as, Cartesian game boards. Furthermore, the constructed distance measures are admissible. We demonstrate how to use the distance measures in an evaluation function of a general game player and show the effectiveness of our approach by comparing with a state-of-the-art player.