Yiming Ye1 and John K. Tsotsos2
1 IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA
2 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
M5S 1A4
Abstract. In this paper we introduce the concept of knowledge granularity and study its influence on an agent's action selection process. Action selection is critical to an agent performing a task in a dynamic, unpredictable environment. Knowledge representation is central to the agent's action selection process. It is important to study what kind of knowledge the agent should represent and the preferred methods of representation. One interesting research issue in this area is the knowledge granularity problem: to what detail should an agent represent a certain kind of knowledge. In other words, how much memory should an agent allocate to represent a certain kind of knowledge？ Here, we first study knowledge granularity and its influence on action selection in the context of an object search agent — a robot that searches for a target within an environment. Then we propose a guideline for selecting reasonable knowledge granularity for an agent in general.
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