Belief revision is the process of rearranging a knowledge base to preserve global consistency whilst accommodating incoming information. Early approaches to belief revision used symbolic model theoretic methods, considering the problem as one of changing a logical theory. More recent approaches have adopted qualitative syntactic methods, taking them into the area of truth maintenance systems, and numerical mathematical methods, thus moving into the mainstream literature of uncertainty management. Multi-agent systems, in which information may come from a variety of human and artificial sources with different degrees of reliability, seem to be a natural domain for belief revision. The aim of this paper is to give a synoptic perspective of this composite subject from the clear air of the high theoretical peaks down to the muddy plain of practical algorithms.
Abstract: Belief revision is the process of rearranging a knowledge base to preserve global consistency whilst accommodating incoming information. Early approaches to belief revision used symbolic model theoretic methods, considering the problem as one of changing a logical theory. More recent approaches have adopted qualitative syntactic methods, taking them into the area of truth maintenance systems, and numerical mathematical methods, thus moving into the mainstream literature of uncertainty management. Multi-agent systems, in which information may come from a variety of human and artificial sources with different degrees of reliability, seem to be a natural domain for belief revision. The aim of this paper is to give a synoptic perspective of this composite subject from the clear air of the high theoretical peaks down to the muddy plain of practical algorithms.
