State Estimation for Game AI Using Particle Filters
Curt Bererton · 2004
Artificial intelligence for games is a challenging area in that it has tight processing constraints and must be fun to play against or with. Some of the latest techniques from robotics research are likely to be applicable to game AI. In particular, we propose that particle filters are well suited to the game AI problem. In this paper, we introduce particle filters, justify their use in game AI, and show the results implemented in a simple game developed using the crystal space game engine.