Rapidly adapting game AI

Current approaches to adaptive game AI require either a high quality of utilised domain knowledge, or a large number of adaptation trials. These requirements hamper the goal of rapidly adapting game AI to changing circumstances. In an alternative, novel approach, domain knowledge is gathered automatically by the game AI, and is immediately (i.e., without trials and without resource-intensive learning) utilised to evoke effective behaviour. In this paper we discuss this approach, called 'rapidly adaptive game AI'. We perform experiments that apply the approach in an actual video game. From our results we may conclude that rapidly adaptive game AI provides a strong basis for effectively adapting game AI in actual video games.