Abstract

Since at least the mid-70's there has been widespread agreement among cognitive science researchers that models of a problem-solving agent should incorporate its knowledge about the world and an inference procedure for interpreting this knowledge to construct plans and take actions. Research questions have focused on how knowledge is represented in computer programs and how such cognitive models can be verified in psychological experiments. We are now experiencing increasing confusion and misunderstanding as different critiques are leveled against this methodology and new jargon is introduced (e.g., "not rules," "ready-to-hand," "background," "situated," "subsymbolic"). Such divergent approaches put a premium on improving our understanding of past modeling methods, allowing us to more sharply contrast proposed alternatives. This paper compares and synthesizes new robotic research that is founded on the idea that knowledge does not consist of objective representations of the world. This research develops a new view of planning that distinguishes between a robot designer's ontological preconceptions, the dynamics of a robot's interaction with an environment, and an observer's descriptive theories of patterns in the robot's behavior. These frame-of-reference problems are illustrated here and unified by a new framework for describing cognitive models.