Using partial satisfaction planning to automatically select NPCs´ goals and generate plans in a simulation game

Controlling and coordinating a large number of Non-Playable Characters (NPCs) is an important challenge in video games. In order to obtain a realistic behaviour, traditional approaches relies hand-written rule based scripts or finite state machines. In the last decade, a new approach to artificial intelligence has emerged. Indeed, automated planning is now a way to control NPCs. However, the gap between theory and application is still quite not filled. In this paper, we address different approaches of i) goal selection and distribution for autonomous planning agents by a coordination module and ii) autonomous goal selection by planning agents using partial satisfaction planning. Our techniques result in a simpler way to coordinate NPCs, still effective in terms of CPU and produce realistic behaviour.