Artificial Intelligence
Computational Intelligence
Constraint propagation algorithms for temporal reasoning: a revised report
Readings in qualitative reasoning about physical systems
An efficient algorithm for managing partial orders in planning
ACM SIGART Bulletin
Querying Temporal Constraint Networks: A Unifying Approach
Applied Intelligence
IEEE Transactions on Knowledge and Data Engineering
Later: Managing Temporal Information Efficiently
IEEE Expert: Intelligent Systems and Their Applications
Artificial Intelligence in Medicine
Reasoning on interval and point-based disjunctive metric constraints in temporal contexts
Journal of Artificial Intelligence Research
Temporal representation and reasoning in artificial intelligence: A review
Mathematical and Computer Modelling: An International Journal
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This paper describes the performance evaluation of six temporal reasoning systems. We show that if you are working with large temporal datasets where information is added incrementally throughout the execution of the program, systems using incompletely connected graphs (i.e., TMM, TimeGraph and TimeGraph-II) seem the best option. While they do not offer the constant query time of systems using fully connected graphs (i.e. the systems based on constraint satisfaction), the savings at assertion time are so substantial that the relatively small performance penalty for queries is a reasonable tradeoff. Of course, these systems do not offer the expressivity of the interval-based systems as they only handle point-based relations. Of the three, TimeGraph-II offers a wider range of qualitative relations as it handles point inequality. It does not currently handle metric information, however, as do TMM and TimeGraph. Thus decisions between these three may be more determined by the reasoning capabilities required rather than raw performance.