A mixed-integer linear programming problem which is efficiently solvable
Journal of Algorithms
Introduction to algorithms
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
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Semiring-based constraint satisfaction and optimization
Journal of the ACM (JACM)
Issues in temporal reasoning for autonomous control systems
AGENTS '98 Proceedings of the second international conference on Autonomous agents
On-line learning in neural networks
On-line learning in neural networks
Parameter adaptation in stochastic optimization
On-line learning in neural networks
Deciding Linear Inequalities by Computing Loop Residues
Journal of the ACM (JACM)
Backtracking algorithms for disjunctions of temporal constraints
Artificial Intelligence
Communications of the ACM
Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Learning Compatibility Coefficients for Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Solving Soft Temporal Constraints: An Experimental Study
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Semiring-Based CSPs and Valued CSPs: Basic Properties and Comparison
Over-Constrained Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Visual exploration and incremental utility elicitation
Eighteenth national conference on Artificial intelligence
Constraint Processing
MAPGEN: Mixed-Initiative Planning and Scheduling for the Mars Exploration Rover Mission
IEEE Intelligent Systems
Low-cost addition of preferences to DTPs and TCSPs
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Constraint solving over semirings
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Tractable Pareto optimization of temporal preferences
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Temporal constraint reasoning with preferences
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Fuzzy conditional temporal problems: Strong and weak consistency
Engineering Applications of Artificial Intelligence
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Managing dynamic CSPs with preferences
Applied Intelligence
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Often we need to work in scenarios where events happen over time and preferences are associated with event distances and durations. Soft temporal constraints allow one to describe in a natural way problems arising in such scenarios. In general, solving soft temporal problems requires exponential time in the worst case, but there are interesting subclasses of problems which are polynomially solvable. In this paper we identify one of such subclasses, that is, simple fuzzy temporal problems with semi-convex preference functions, giving tractability results. Moreover, we describe two solvers for this class of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and robustness. Sometimes, however, temporal local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preferences, we show that machine learning techniques can be useful in this respect. In particular, we present a learning module based on a gradient descent technique which induces local temporal preferences from global ones. We also show the behavior of the learning module on randomly-generated examples.