A theory of diagnosis from first principles
Artificial Intelligence
A correction to the algorithm in Reiter's theory of diagnosis
Artificial Intelligence
A theory of measurement in diagnosis from first principles
Artificial Intelligence
A variant of Reiter's hitting-set algorithm
Information Processing Letters
Discrete Mathematical Structures
Discrete Mathematical Structures
Generating Diagnoses from Conflict Sets
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Computational aspects of monotone dualization: A brief survey
Discrete Applied Mathematics
Generating Diagnoses from Conflict Sets with Continuous Attributes
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Operator component matrix model for IMP program diagnosis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
How to apply SAT-solving for the equivalence test of monotone normal forms
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
An efficient diagnosis algorithm for inconsistent constraint sets
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Hi-index | 0.89 |
In model-based diagnosis or other research fields, the hitting sets of a set cluster are usually used. In this paper we introduce some algorithms, including the new BHS-tree and Boolean algebraic algorithms. In the BHS-tree algorithm, a binary-tree is used for the computation of hitting sets, and in the Boolean algebraic algorithm, components are represented by Boolean variables. It runs just for one time to catch the minimal hitting sets. We implemented the algorithms and present empirical results in order to show their superiority over other algorithms for computing hitting sets.