We present an alternative form AI that avoids limited type of interaction, namely the AI agents acting independently of each other rather than working together as a team.  To do so, we add the multi-agent functionality to the AI for a simple pursuit game.  Initially the AI directs each agent independently to pursue the target player.  These agents then suffer from collision and overlapping such that the player can evade the clustered agents without difficulty.  Next we introduce our multi-agent AI that coordinates the efforts of the enemy agents so that they stay in formation and work together to corner the player.  In so doing we wish to show that this greatly improves the quality of gameplay and the realism simulated by the AI.  Further, this upholds the original intention of the AI as designed by the developers and avoids unrealistic “cheats” to circumvent the intended gameplay.  While this research is centered in gaming, we also believe that it has further reaching applications in security, simulations, and robotics.
['D. Michael Franklin', 'Kevin L. Markley']
Multi-Agent Artificial Intelligence in Pursuit Strategies: Breaking through the Stalemate