Abstract
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. Adopting a classical AGM-style belief revision operator is problematic, since it implies that, as a result of perception, the agent will come to believe everything that follows from its new set of beliefs. We overcome this difficulty 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.
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Ismail, H.O., Kasrin, N. (2010). Focused Belief Revision as a Model of Fallible Relevance-Sensitive Perception. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds) KI 2010: Advances in Artificial Intelligence. KI 2010. Lecture Notes in Computer Science(), vol 6359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16111-7_14
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DOI: https://doi.org/10.1007/978-3-642-16111-7_14
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