The Implementation of a First-Order Logic AGM Belief Revision System

Belief revision is increasingly being seen as central to a number of fundamental problems in Artificial Intelligence such as nonmonotonic reasoning, reasoning about action, truth maintenance and database update. This paper describes the first implementation of an AGM belief revision system. The system is based on classical firstorder logic, and for any finitely representable belief state, it efficiently computes expansions, contractions and revisions satisfying the AGM postulates for rational belief change. The system uses a finite base to represent a belief set, and interprets a partially specified entrenchment as representing a unique `most conservative' entrenchment -- this is motivated by considerations of evidence and by the close connections between belief revision and nonmonotonic reasoning. We describe in detail the algorithms for belief change, and give some examples of the system's operation