Fast approximation algorithms for multicommodity flow problems
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Fast approximation algorithms for fractional packing and covering problems
Mathematics of Operations Research
Fast planning through planning graph analysis
Artificial Intelligence
Back to the Future for Consistency-Based Trajectory Tracking
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Partition-Based Lower Bound for Max-CSP
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
An Information-Theoretic Characterization of Abstraction in Diagnosis and Hypothesis Selection
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Reformulating Combinatorial Optimization as Constraint Satisfaction
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
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Constraint satisfaction and combinatorial optimization form the crux of many AI problems. In constraint satisfaction, feasibility-reasoning mechanisms are used to prune the search space, while optimality-reasoning is used for combinatorial optimization. Many AI tasks related to diagnosis, trajectory tracking and planning can be formulated as hybrid problems containing both satisfaction and optimization components, and can greatly benefit from a proper blend of these independently powerful techniques.We introduce the notion of model counting to bridge the gap between feasibilityand optimality-reasoning. The optimization part of a problem then becomes a search for the right set of constraints that must be satisfied in any good solution. These constraints, which we call the oracular constraints, replace the optimization component of a problem to revive the power of constraint reasoning systems.