Agent Abilities in a Landmark-based Mapping Model

We present a landmark-based model for agents to uniquely learn a partial map of an unknown environment. Our definition of landmark recognition and agent abilities allow different agents to construct different maps. We define landmarks in terms of feature sets that tie the various agent abilities into the landmark recognition process. The ultimate goal of having agents learn partial and unique maps is to promote different behavioral outcomes from higher level planning. Our implementation focuses on providing unexpected and believable Non-Player Characters (NPC) in modern games.