Open Constraint Satisfaction

Faltings, Boi; Macho-Gonzalez, Santiago

doi:10.1007/3-540-46135-3_24

Cited by 24 publications

(13 citation statements)

References 15 publications

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“…3. F-O-Opt (failure-driven algorithm for Open Hidden-variable Weighted Constraint Optimization Problems) which is one of the algorithm proposed in [5] for open constraint optimization. The context for this search method is dynamic and constraints are updated while searching so used learning technics are local and different.…”

Section: Related Workmentioning

confidence: 99%

YIELDS: A Yet Improved Limited Discrepancy Search for CSPs

Karoui

Huguet

López

et al. 2007

Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems

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Abstract. In this paper, we introduce a Yet ImprovEd Limited Discrepancy Search (YIELDS), a complete algorithm for solving Constraint Satisfaction Problems. As indicated in its name, YIELDS is an improved version of Limited Discrepancy Search (LDS). It integrates constraint propagation and variable order learning. The learning scheme, which is the main contribution of this paper, takes benefit from failures encountered during search in order to enhance the efficiency of variable ordering heuristic. As a result, we obtain a search which needs less discrepancies than LDS to find a solution or to state a problem is intractable. This method is then less redundant than LDS. The efficiency of YIELDS is experimentally validated, comparing it with several solving algorithms: Depth-bounded Discrepancy Search, Forward Checking, and Maintaining Arc-Consistency. Experiments carried out on randomly generated binary CSPs and real problems clearly indicate that YIELDS often outperforms the algorithms with which it is compared, especially for tractable problems.

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Section: Related Workmentioning

confidence: 99%

YIELDS: A Yet Improved Limited Discrepancy Search for CSPs

Karoui

Huguet

López

et al. 2007

Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems

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“…Open constraints were introduced in [7] in the context of distributed reasoning but with the goal of minimizing the number of requests needed to complete the definition. Solver learning has been introduced in [3] with a special rule system.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

“…In this paper, we propose to use partially defined finite domain constraints called Open Constraints. In an Open Constraint [7], some tuples are known to be true, some other are known to be false and some are just unknown.…”

Section: Introductionmentioning

confidence: 99%

Consistency for Partially Defined Constraints

Legtchenko

Lallouet

2005

Principles and Practice of Constraint Programming - CP 2005

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Abstract. Partially defined or Open Constraints can be used to model the incomplete knowledge of a concept or a relation. We propose to complete its definition by using Machine Learning techniques. Our technique is composed of two steps: first we learn a classifier for the constraint's projections and then we transform the classifier into a propagator. We show that our technique not only has good learning performances but also yields a very efficient solver for the learned constraint.

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“…Instead, most existing approaches, with the exception of the Open CSP approach of Faltings and Gonzalez [7], have focused on batch problem solving. A batch approach is potentially inefficient in an incremental setting because it fails to take advantage of the existing solution.…”

Section: Introductionmentioning

confidence: 99%

Bumping strategies for the multiagent agreement problem

Modi

Veloso

2005

Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multiagent Systems

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We introduce the Multiagent Agreement Problem (MAP) to represent a class of multiagent scheduling problems. MAP is based on the Distributed Constraint Reasoning (DCR) paradigm and requires agents to choose values for variables to satisfy not only their own constraints, but also equality constraints with other agents. The goal is to represent problems in which agents must agree on scheduling decisions, for example, to agree on the start time of a meeting. We investigate a challenging class of MAP -private, incremental MAP (piMAP) in which agents do incremental scheduling of activities and there exist privacy restrictions on information exchange. We investigate a range of strategies for piMAP, called "bumping" strategies. We empirically evaluate these strategies in the domain of calendar management where a personal assistant agent must schedule meetings on behalf of its human user. Our results show that bumping decisions based on scheduling difficulty models of other agents can significantly improve performance over simpler bumping strategies.

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Open Constraint Satisfaction

Cited by 24 publications

References 15 publications

YIELDS: A Yet Improved Limited Discrepancy Search for CSPs

YIELDS: A Yet Improved Limited Discrepancy Search for CSPs

Consistency for Partially Defined Constraints

Bumping strategies for the multiagent agreement problem

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