Artificial Intelligence
Artificial Intelligence - Special issue on knowledge representation
Reasoning about qualitative temporal information
Artificial Intelligence - Special volume on constraint-based reasoning
A metric time-point and duration-based temporal model
ACM SIGART Bulletin
Combining qualitative and quantitative constraints in temporal reasoning
Artificial Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Synthesizing constraint expressions
Communications of the ACM
A Generalized Framework for Reasoning with Multi-Point Events
ASIAN '97 Proceedings of the Third Asian Computing Science Conference on Advances in Computing Science
Qualitative temporal reasoning with points and durations
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Annals of Mathematics and Artificial Intelligence
On point-duration networks for temporal reasoning
Artificial Intelligence
INDU: An Interval and Duration Network
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Some Observations on Durations, Scheduling and Allen's Algebra
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
Reasoning on interval and point-based disjunctive metric constraints in temporal contexts
Journal of Artificial Intelligence Research
A new framework for reasoning about points, intervals and durations
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Computational complexity of linear constraints over the integers
Artificial Intelligence
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A duration is known as a time distance between two point events. This relationship has recently been formalized as the point duration network (PDN) in (Navarrete & Marin 1997). However, only the qualitative information about points and durations was considered. This paper presents an augmented point duration network (APDN) to represent both qualitative and quantitative information about point events. We further extend APDN to capture quantitative information about durations. We propose algorithms to solve reasoning tasks such as determining satisfiability of the network, and finding a consistent scenario with minimal domains. Thus, we present an expressively richer framework than the existing ones to handle both qualitative and quantitative information about points as well as durations.