A motivational architecture of action selection for non-player characters in dynamic environments
Résumé
In the wake of today's drive for increasingly realistic video game environments, the credibility of non-player characters (NPC) behavior has become an important issue. This paper describes MHiCS (Motivational and Hierarchical with Classifier Systems), a control architecture for action selection that can be embedded in such characters. It implements mechanisms stemming from the animat approach, which aims at conceiving autonomous and adaptable entities able to survive in unpredictable environments. The originality of MHiCS lies in its combination of a motivation mechanism with intelligible condition-action rules (Classifiers Systems) and online unsupervised learning processes. It has been successfully tested in the Half Life game, against efficient hand-tuned NPC. This work contributes to the design of Classifiers Systems operating in non-deterministic environments and to the improvement of decisional autonomy in non-player characters.