Approximate belief revision
Log J IGPL (2001) 9 (6): 755-768.
01 November 2001
The standard theory for belief revision provides an elegant and powerful framework for reasoning about how a rational agent should change its beliefs when confronted with new information. However, the agents considered are extremely idealized. Some recent models attempt to tackle the problem of plausible belief revision by adding structure to the belief bases and using nonstandard inference operations. One of the key ideas is that not all of an agent's beliefs are relevant for an operation of belief change.
In this paper we incorporate the insights pertaining to local change and relevance sensitivity with the use of approximate inference relations. These approximate inference relations offer us partial solutions at any stage of the revision process. The quality of the approximations improves as we allow for more and more resources to be used. We are provided with upper and lower bounds to what would be obtained with the use of classical inference.
AKnowledge Systems Group, School of Computer Science and Engineering, University of New South Wales, Sydney, Australia E-mail: email@example.com A2Brooklyn College of CUNY and CUNY Graduate Center, New York, NY, USA E-mail: firstname.lastname@example.org A3Department of Computer Science, University of São Paulo, Brazil E-mail: email@example.com