The Design and Experimental Analysis of Algorithms for Temporal Reasoning
P. vanBeek, D. W. Manchak

TL;DR
This paper presents the design and empirical evaluation of algorithms for temporal reasoning based on Allen's framework, improving efficiency and scalability for practical applications.
Contribution
It introduces optimized path consistency and backtracking algorithms with heuristics and reformulations that significantly enhance performance in temporal reasoning tasks.
Findings
Up to ten-fold speedup in path consistency algorithm
Heuristics dramatically improve backtracking performance
Reformulation reduces time and space requirements
Abstract
Many applications -- from planning and scheduling to problems in molecular biology -- rely heavily on a temporal reasoning component. In this paper, we discuss the design and empirical analysis of algorithms for a temporal reasoning system based on Allen's influential interval-based framework for representing temporal information. At the core of the system are algorithms for determining whether the temporal information is consistent, and, if so, finding one or more scenarios that are consistent with the temporal information. Two important algorithms for these tasks are a path consistency algorithm and a backtracking algorithm. For the path consistency algorithm, we develop techniques that can result in up to a ten-fold speedup over an already highly optimized implementation. For the backtracking algorithm, we develop variable and value ordering heuristics that are shown empirically to…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSemantic Web and Ontologies · Logic, Reasoning, and Knowledge · Constraint Satisfaction and Optimization
