Artificial Intelligence - Special issue on knowledge representation
Possibilistic constraint satisfaction problems or “how to handle soft constraints?”
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Selected papers from the Joint ERCIM/Compulog Net Workshop on New Trends in Contraints
Learning Preferences on Temporal Constraints: A Preliminary Report
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Temporal constraint reasoning with preferences
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
The algebra IAfuz: a framework for qualitative fuzzy temporal reasoning
Artificial Intelligence
AI Communications - Constraint Programming for Planning and Scheduling
Solving temporal over-constrained problems using fuzzy techniques
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Marco Somalvico Memorial Issue
Journal of Artificial Intelligence Research
Temporal reasoning with preferences and uncertainty
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The algebra IAfuz: a framework for qualitative fuzzy temporal reasoning
Artificial Intelligence
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Discriminating exanthematic diseases from temporal patterns of patient symptoms
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Soft temporal constraints problems allow for a natural description of scenarios where events happen over time and preferences are associated with event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem, and then to learn from them suitable preferences over distances and durations. In this paper, we describe our learning algorithm and we show its behaviour on classes of randomly generated problems. Moreover, we also describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we give experimental results to compare them.