GenC: A Fast Tool for Applications Involving Belief Revision

GenC: A Fast Tool for Applications Involving Belief Revision

Aaron Hunter, John Agapeyev

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

The process of belief revision occurs in many applications where agents may have incorrect or incomplete information. One important theoretical model of belief revision is the well-known AGM approach. Unfortunately, there are few tools available for solving AGM revision problems quickly; this has limited the use of AGM operators for practical applications. In this demonstration paper, we describe GenC, a tool that is able to quickly calculate the result of AGM belief revision for formulas with hundreds of variables and millions of clauses. GenC uses an AllSAT solver and parallel processing to solve revision problems at a rate much faster than existing systems. The solver works for the class of parametrised difference operators, which is an extensive class of revision operators that use a weighted Hamming distance to measure the similarity between states. We demonstrate how GenC can be used as a stand-alone tool or as a component of a reasoning system for a variety of applications.
Keywords:
Knowledge Representation and Reasoning: general
Multi-agent Systems: general
Uncertainty in AI: general