Computational Intelligence
Constraint propagation algorithms for temporal reasoning: a revised report
Readings in qualitative reasoning about physical systems
Exact and approximate reasoning about temporal relations
Computational Intelligence
Temporally distributed symptoms in technical diagnosis
Temporally distributed symptoms in technical diagnosis
Temporal reasoning and planning
Reasoning about plans
Reasoning about qualitative temporal information
Artificial Intelligence - Special volume on constraint-based reasoning
Complexity and algorithms for reasoning about time: a graph-theoretic approach
Journal of the ACM (JACM)
Journal of the ACM (JACM)
Planning with continuous change
Planning with continuous change
Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra
Journal of the ACM (JACM)
Efficient algorithms for qualitative reasoning about time
Artificial Intelligence
Combining qualitative and quantitative constraints in temporal reasoning
Artificial Intelligence
Twenty-one large tractable subclasses of Allen's algebra
Artificial Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
A comparison of point-based approaches to qualitative temporal reasoning
Artificial Intelligence
Combining topological and size information for spatial reasoning
Artificial Intelligence
Constraint Processing
Handbook of Temporal Reasoning in Artificial Intelligence (Foundations of Artificial Intelligence (Elsevier))
Applications of SHOP and SHOP2
IEEE Intelligent Systems
The 3rd international planning competition: results and analysis
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
The design and experimental analysis of algorithms for temporal reasoning
Journal of Artificial Intelligence Research
Managing efficiently temporal relations through indexed spanning trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Efficient computation of minimal point algebra constraints by metagraph closure
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Reasoning about qualitative temporal information
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Temporal reasoning in sequence graphs
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A simple way to improve path consistency processing in interval algebra networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
A representation for efficient temporal reasoning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Temporal query processing with indefinite information
Artificial Intelligence in Medicine
Solving minimal constraint networks in qualitative spatial and temporal reasoning
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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Computing the minimal representation of a given set of constraints (a CSP) over the Point Algebra (PA) is a fundamental temporal reasoning problem. The main property of a minimal CSP over PA is that the strongest entailed relation between any pair of variables in the CSP can be derived in constant time. We study some new methods for solving this problem which exploit and extend two prominent graph-based representations of a CSP over PA: the timegraph and the series-parallel (SP) metagraph. Essentially, these are graphs partitioned into sets of chains and series-parallel subgraphs, respectively, on which the search is supported by a metagraph data structure. The proposed approach is based on computing the metagraph closure for these representations, which can be accomplished by some methods studied in the paper. In comparison with the known techniques based on enforcing path consistency, under certain conditions about the structure of the input CSP and the size of the generated metagraph, the proposed metagraph closure approach has better worst-case time and space complexity. Moreover, for every sparse CSP over the convex PA, the time complexity is reduced to O(n^2) from O(n^3), where n is the number of variables involved in the CSP. An extensive experimental analysis presented in the paper compares the proposed techniques and other known algorithms. These experimental results identify the best performing methods and show that, in practice, for CSPs exhibiting chain or SP-graph structure and randomly generated (both sparse and dense) CSPs, the metagraph closure approach is significantly faster than the approach based on enforcing path consistency.