Combining qualitative and quantitative constraints in temporal reasoning

Authors:
Itay Meiri
Affiliations:
Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA
Venue:
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Year:
1991

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Abstract

This paper presents a general model for temporal reasoning, capable of handling both qualitative and quantitative information. This model allows the representation and processing of all types of constraints considered in the literature so far, including metric constraints (restricting the distance between time points), and qualitative, disjunctive, constraints (specifying the relative position between temporal objects). Reasoning tasks in this unified framework are formulated as constraint satisfaction problems, and are solved by traditional constraint satisfaction techniques, such as backtracking and path consistency. A new class of tractable problems is characterized, involving qualitative networks augmented by quantitative domain constraints, some of which can be solved in polynomial time using arc and path consistency.