Planning for conjunctive goals
Artificial Intelligence
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Temporal planning with continuous change
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Single machine scheduling subject to precedence delays
Discrete Applied Mathematics
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Computers and Intractability: A Guide to the Theory of NP-Completeness
Computing the Envelope for Stepwise-Constant Resource Allocations
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
On finding a solution in temporal constraint satisfaction problems
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Temporal Reasoning in Nested Temporal Networks with Alternatives
Recent Advances in Constraints
Augmenting disjunctive temporal problems with finite-domain constraints
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
A constraint model for state transitions in disjunctive resources
CSCLP'06 Proceedings of the constraint solving and contraint logic programming 11th annual ERCIM international conference on Recent advances in constraints
Conditional and composite temporal CSPs
Applied Intelligence
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This paper introduces the concept of Resource Temporal Network (RTN), a constraint network that subsumes both classical attributes used in A.I. Planning and capacity resources traditionally handled in Scheduling. After giving a formal definition of RTNs, we analyze their expressive power and study complexities of several fragments of the RTN framework. We show that solving an RTN is in general NP-Complete - which is not surprising given the expressivity of the framework - whereas computing a Necessary Truth Criterion is polynomial. This last result opens the door for promising algorithms to solve RTNs.