  In this paper we propose an architecture for specifying the interaction of
non-player characters (NPCs) in the game-world in a way that abstracts common
tasks in four main conceptual components, namely perception, deliberation,
control, action. We argue that this architecture, inspired by AI research on
autonomous agents and robots, can offer a number of benefits in the form of
abstraction, modularity, re-usability and higher degrees of personalization for
the behavior of each NPC. We also show how this architecture can be used to
tackle a simple scenario related to the navigation of NPCs under incomplete
information about the obstacles that may obstruct the various way-points in the
game, in a simple and effective way.
