A KNOWLEDGE REPRESENTATION FOR CONSTRAINT SATISFACTION PROBLEMS

In this paper we present a general representation for constraint satisfaction problems (CSP) and a - framework for reasoning about their solution that unlike most constraint-based relaxation algorithms. stresses the need for a "natural" encoding of constraint knowledge and can facilitate making inferences for propagation, backtracking, and explanation. The representation consists of two components: a generate-and-test problem solver which contains information about the problem variables, and a constraint-driven reasoner that manages a set of constraints, specified as arbitrarily complex Boolean expressions and represented in the form of a constraint network. This constraint network: incorporates control information (reflected in 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.Information Systems Working Papers Serie

Solving Set Constraint Satisfaction Problems using ROBDDs

In this paper we present a new approach to modeling finite set domain constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We show that it is possible to construct an efficient set domain propagator which compactly represents many set domains and set constraints using ROBDDs. We demonstrate that the ROBDD-based approach provides unprecedented flexibility in modeling constraint satisfaction problems, leading to performance improvements. We also show that the ROBDD-based modeling approach can be extended to the modeling of integer and multiset constraint problems in a straightforward manner. Since domain propagation is not always practical, we also show how to incorporate less strict consistency notions into the ROBDD framework, such as set bounds, cardinality bounds and lexicographic bounds consistency. Finally, we present experimental results that demonstrate the ROBDD-based solver performs better than various more conventional constraint solvers on several standard set constraint problems