Artificial Intelligence
Motivation analysis, abductive unification, and nonmonotonic equality
Artificial Intelligence
Explanation and prediction: an architecture for default and abductive reasoning
Computational Intelligence
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Constraint propagation algorithms for temporal reasoning: a revised report
Readings in qualitative reasoning about physical systems
MOMO—model-based diagnosis for everybody
Proceedings of the sixth conference on Artificial intelligence applications
The computational complexity of abduction
Artificial Intelligence - Special issue on knowledge representation
Artificial Intelligence - Special issue on knowledge representation
A spectrum of logical definitions of model-based diagnosis
Computational Intelligence
Abduction versus closure in causal theories
Artificial Intelligence
The complexity of logic-based abduction
Journal of the ACM (JACM)
On the computational complexity of querying bounds on differences constraints
Artificial Intelligence
A spectrum of definitions for temporal model-based diagnosis
Artificial Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Using Compiled Knowledge to Guide and Focus Abductive Diagnosis
IEEE Transactions on Knowledge and Data Engineering
Later: Managing Temporal Information Efficiently
IEEE Expert: Intelligent Systems and Their Applications
LaTeR: A General Purpose Manager of Temporal Information
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
A survey on knowledge compilation
AI Communications
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Generating abductive explanations is the basis of several problem solving activities such as diagnosis, planning, and interpretation. Temporal abduction means generating explanations that do not only account for the presence of observations, but also for temporal information on them, based on temporal knowledge in the domain theory. We focus on the case where such a theory contains temporal constraints that are required to be consistent with temporal information on observations. The aim of this paper is to propose efficient algorithms for computing temporal abductive explanations. Temporal constraints in the theory and in the observations can be used actively by an abductive reasoner in order to prune inconsistent candidate explanations at an early stage during their generation. However, checking temporal constraint satisfaction frequently generates some overhead. In the paper, we analyze two incremental ways of making this process efficient. First we show how, using a specific class of temporal constraints (which is expressive enough for many applications), such an overhead can be reduced significantly, yet preserving a full pruning power. In general, the approach does not affect the asymptotic complexity of the problem, but it provides significant advantages in practical cases. We also show that, for some special classes of theories, the asymptotic complexity is also reduced. We then show how, compiled knowledge based on temporal information, can be used to further improve the computation, thus, extending to the temporal framework previous results in the case of atemporal abduction. The paper provides both analytic and experimental evaluations of the computational advantages provided by our approaches.