A theory of diagnosis from first principles
Artificial Intelligence
The complexity of logic-based abduction
Journal of the ACM (JACM)
Horn approximations of empirical data
Artificial Intelligence
On the complexity of dualization of monotone disjunctive normal forms
Journal of Algorithms
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
On computing all abductive explanations
Eighteenth national conference on Artificial intelligence
Discovering all most specific sentences
ACM Transactions on Database Systems (TODS)
Discovery of minimal unsatisfiable subsets of constraints using hitting set dualization
PADL'05 Proceedings of the 7th international conference on Practical Aspects of Declarative Languages
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We consider the problem of enumerating minimal explanations in propositional theory. We propose a new way of characterizing the enumeration problem in terms of not only the number of explanations, but also the number of unexplanations. Maximal unexplanations are a maximal set of abducible formulas which cannot explain the observation given a background theory. In this paper, we interleavingly enumerate not only minimal explanations but also maximal unexplanations. To best of our knowledge, there has been no algorithm which is characterized in terms of such maximal unexplanations. We propose two algorithms to perform this task and also analyze them in terms of query complexity, space complexity and time complexity.