Development of an incremental pattern extraction based Gomoku agent

Abstract

The subject of this paper is an unusual approach to artificial game playing. Our main goal is to replace exhaustive game tree search with incremental pattern extraction and recognition, thus greatly reducing computation time. This is achieved using search with a depth of 3, together with pattern matching and pattern-based heuristic functions, where patterns are learned through play. We examine the efficiency and efficacy of this method regarding the game Gomoku, also known as Five-in-a-row. To evaluate our agent, we implement two basic reference agents and also incorporate a strong open-source AI called "Carbon" into our environment.