Tractable reasoning via approximation
Artificial Intelligence
Artificial Intelligence
Measuring inconsistency in knowledge via quasi-classical models
Eighteenth national conference on Artificial intelligence
How to act on inconsistent news: ignore, resolve, or reject
Data & Knowledge Engineering
Measuring inconsistency in knowledgebases
Journal of Intelligent Information Systems
Computational Linguistics
Analysing inconsistent first-order knowledgebases
Artificial Intelligence
An Algorithm for Computing Inconsistency Measurement by Paraconsistent Semantics
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Approaches to measuring inconsistent information
Inconsistency Tolerance
Measuring inconsistency in requirements specifications
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Measuring incompleteness under multi-valued semantics by partial MaxSAT solvers
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Hi-index | 0.00 |
Measuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first analyze its computational complexity. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximation of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm.