New Approximate Strategies for Playing Sum Games Based on Subgame Types

Abstract

In this work, we investigate the potential of combining artificial intelligence (AI) tree-search algorithms with the algorithms of combinatorial game theory to provide more efficient strategies for playing sum games based on subgame types. Two new approximate strategies are developed and tested using a specified game model. Both strategies achieve higher performance than approximate strategies previously proposed in literature without being computationally more expensive