Prioritised fuzzy constraint satisfaction problems: axioms, instantiation and validation
Fuzzy Sets and Systems - Theme: Multicriteria decision
Towards Fast Vickrey Pricing using Constraint Programming
Artificial Intelligence Review
ADOPT-ing: unifying asynchronous distributed optimization with asynchronous backtracking
Autonomous Agents and Multi-Agent Systems
Dynamic Backtracking for Distributed Constraint Optimization
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Generic preferences over subsets of structured objects
Journal of Artificial Intelligence Research
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In the frame of classical Constraint Satisfaction Problems (CSPs), the backtrack tree search, combined with learning methods, presents a double advantage: for static solving, it improves the search speed by avoiding redundant explorations; for dynamic solving (after a slight change of the problem), it reuses the previous searches to build a new solution quickly. Backtrack reasoning concludes the reject of certain combinatorial choices. Nogood Recording memorizes these choices in order not to reproduce.We aim to use Nogood Recording in the wider scope of the Valued CSP framework (VCSP) to enhance the branch and bound algorithm. Therefore, nogoods are used to increase the lower bound used by the branch and bound to prune the search. This issue leads to the definition of the "Valued Nogoods" and their use.This study focuses particularly on penalty and dynamic VCSPs which require special developments. However; our results give an extension of the Nogood Recording to the general VCSP framework.