Fuzzy coordinator based intelligent agents for team coordination behavior in close combat games

Nowadays, many commercial computer games on the market have employed some AI techniques especially on the behaviors of NPCs (Non-Playable Characters). The aim is to make the NPCs game more human-like, immersive or natural. In a close combat game, creating strong AI forces require team coordination of NPCs. By employing an AI based agent (i.e. intelligent agent) for team coordination of NPCs leads to a deeper sense of immersion since NPCs are allowed to work together to produce better tactics and strategies. This technique faces many challenges since the intelligent agents are based on state transitions. The state transition leads to the decision for selecting the appropriate behavior by itself. Therefore, sometimes the selection could be failed to accomplish a team objective. This is due to weak coordination between NPCs and the intelligent agent. This paper describes the Fuzzy coordinator method to support the intelligent agent for selecting suitable behavior for each NPC. The intelligent agent who becomes the team leader will monitor and analyze health of each NPC, which one is strong and weak, which one has to back off or stays in the battle. The experimental result demonstrates that by employing the above technique, NPC behavior selection lead to appropriate coordination from leader which represented by keeping the health parameter 75 % and 50 % at maximum and minimum respectively.