Coordination of NPCs in multi-agent systems based on behavior trees

Abstract. This paper addresses the design and coordination challenges of controlling non-player characters' (NPCs) behaviors in multi-agent systems (MAS) using behavior trees (BTs), which are preferred over finite state machines (FSMs) due to their hierarchical structure and ease of maintenance. While BTs effectively resolve the question of "What to do next?" for individual NPCs, their application in MAS, particularly with the integration of a blackboard system for central control, reveals limitations in efficiency, robustness, and heuristic capacity as system complexity increases. To explore solutions to these challenges, this study analyzes various algorithms that enhance the functionality of behavior trees within MAS. The research focuses on three primary areas: the optimization of behavior trees, the development of behavior tree search algorithms, and the improvement of communication algorithms within BTs. Methods involve a comparative analysis of existing and new algorithmic approaches to identify and address inefficiencies in NPC coordination. The findings indicate that advanced behavior tree configurations, when combined with innovative search and communication strategies, significantly improve the coordination, robustness, and operational efficiency of MAS. These enhancements allow for more dynamic and responsive NPC interactions in complex gaming environments.