High-Level Perception as Focused Belief Revision
Haythem O. Ismail, Nasr Kasrin · Frontiers in artificial intelligence and applications · 2010
We present a framework for incorporating perception-induced beliefs into the knowledge base of a rational agent. Normally, the agent accepts the propositional content of perception and other propositions that follow from it. Given the fallibility of perception, this may result in contradictory beliefs. Hence, we model high-level perception as belief revision. We overcome difficulties imposed by the highly idealistic classical belief revision in two ways. First, we adopt a belief revision operator based on relevance logic, thus limiting the derived beliefs to those that relevantly follow from the new percept. Second, we focus belief revision on only a subset of the agent's set of beliefs—those that we take to be within the agent's current focus of attention.