Multi-Agent Pursuit-Evasion Game Based on Organizational Architecture

Multi-agent coordination mechanisms are frequently used in pursuit-evasion games with the aim of enabling the coalitions of the pursuers and unifying their individual skills to deal with the complex tasks encountered. In this paper, we propose a coalition formation algorithm based on organizational principles and applied to the pursuit-evasion problem. In order to allow the alliances of the pursuers in different pursuit groups, we have used the concepts forming an organizational modeling framework known as YAMAM (Yet Another Multi Agent Model). Specifically, we have used the concepts Agent, Role, Task, and Skill, proposed in this model to develop a coalition formation algorithm to allow the optimal task sharing. To control the pursuers' path planning in the environment as well as their internal development during the pursuit, we have used a Reinforcement learning method (Q-learning). Computer simulations reflect the impact of the proposed techniques. ACM CCS (2012) Classification: Computing methodologies → Artificial intelligence → Distributed artificial intelligence → Multi-agent systems Theory of computation → Theory and algorithms for application domains → Algorithmic game theory and mechanism design → Convergence and learning in games.