A Novel Decision-Making Model for Playing Board Game Combining Planning and Opponent Behaviors

Abstract

Board game offers a unique platform for exploring the capabilities of artificial intelligence in decision-making. It demands long-term strategic planning and opponent behaviors to refine decision-making. Since the success of AlphaGo family, learning agents have become pivotal methods for board game. However, current learning agents rarely incorporate planners or build interactive loops with opponents’ behaviors in decision-making. This paper proposes a novel planning-based model (BG-Planner) for strategic decision-making and long-term planning in board game. We propose a Graphplan-style network with alternating action and proposition layers to predict actions and assess wining rate. Further, an opponent modeling strategy is incorporated to predict opponent behaviors, assist decision-making and reduce competitive uncertainty. We also introduce a knowledge-based search tactic to enhance BG-Planner’s learning. Experimental results demonstrate that BG-Planner enhances the quality and efficiency of decision-making in the Gomoku game. It shows potential to improve deep planning strategies in decision-making intelligence.