A new polynomial-time algorithm for linear programming
Combinatorica
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
A quantitative approach to logical inference
Decision Support Systems
Constraint relaxation may be perfect
Artificial Intelligence
Rule-based versus structure-based models for explaining and generating expert behavior
Communications of the ACM
Synthesizing constraint expressions
Communications of the ACM
Ramp activity expert system for scheduling and co-ordination at an airport
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Querying a polynomial object-relational constraint database in model-based diagnosis
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
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We present a general representation for problems that can be reduced to constraint satisfaction problems (CSP) and a model for reasoning about their solution. The novel part of the model is a constraint-driven reasoner that manages a set of constraints specified in terms of arbitrarily complex Boolean expressions and represented in the form of a dependency network. This dependency network incorporates control information (derived from the syntax of the constraints) that is used for constraint propagation, contains dependency information that can be used for explanation and for dependency-directed backtracking, and is incremental in the sense that if the problem specification is modified, a new solution can be derived by modifying the existing solution. The constraint-driven reasoner is coupled to a problem solver which contains information about the problem variables and preference orderings.