Belief Base Revision for Further Improvement of Unified Answer Set Programming
Kumar Sankar Ray, Sandip Paul, Diganta Saha
TL;DR
This paper introduces a belief base revision method using Unified Answer Set Programs to handle imprecise and uncertain information, enhancing nonmonotonic reasoning capabilities.
Contribution
It develops a new belief base revision operator based on Removed Set Revision strategy for Unified Answer Set Programs.
Findings
The revision operator satisfies key postulates for belief base revision.
It effectively manages imprecise and uncertain information.
Enhances nonmonotonic reasoning with belief revision.
Abstract
A belief base revision is developed. The belief base is represented using Unified Answer Set Programs which is capable of representing imprecise and uncertain information and perform nonomonotonic reasoning with them. The base revision operator is developed using Removed Set Revision strategy. The operator is characterized with respect to the postulates for base revisions operator satisfies.
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Bayesian Modeling and Causal Inference · Multi-Agent Systems and Negotiation