Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Making compromises among antagonist constraints in a planner
Artificial Intelligence
Artificial Intelligence
Arc and path consistence revisited
Artificial Intelligence
Some fundamental properties of local constraint propagation
Artificial Intelligence
Epistemic entrenchment and possibilistic logic
Artificial Intelligence
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
FAIR '91 Proceedings of the International Workshop on Fundamentals of Artificial Intelligence Research
Performance measurement and analysis of certain search algorithms.
Performance measurement and analysis of certain search algorithms.
Revisions of knowledge systems using epistemic entrenchment
TARK '88 Proceedings of the 2nd conference on Theoretical aspects of reasoning about knowledge
Partial constraint satisfaction
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Nonmonotonic logics: meaning and utility
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
An efficient arc consistency algorithm for a class of CSP problems
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
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Many AI synthesis problems such as planning or scheduling may be modelized as constraint satisfaction problems (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks such as job-shop scheduling, time-table scheduling, design..., all these constraints have not the same significance and have not to be necessarily satisfied. A first distinction can be made between hard constraints, which every solution should satisfy and soft constraints, whose satisfaction has not to be certain, In this paper, we formalize the notion of possibilistic constraint satisfaction problems that allows the modeling of uncertainly satisfied constraints. We use a possibility distribution over labelings to represent respective possibilities of each labeling. Necessity-valued constraints allow a simple expression of the respective certainty degrees of each constraint. The main advantage of our approach is its integration in the CSP technical framework. Most classical techniques, such as Backtracking (BT), arc-consistency enforcing (AC) or Forward Checking have been extended to handle possibilistics CSP and are effectively implemented. The utility of our approach is demonstrated on a simple design problem.