Constraint satisfaction from a deductive viewpoint (Research Note)
Artificial Intelligence
Tree clustering for constraint networks (research note)
Artificial Intelligence
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Decomposing constraint satisfaction problems using database techniques
Artificial Intelligence
On the conversion between non-binary constraint satisfaction problems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Over-Constrained Systems
Constraint-Directed Backtracking
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Dynamic Constraint Weighting for Over-Constrained Problems
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Constraint structure in constraint satisfaction problems
Constraint structure in constraint satisfaction problems
Solving time-dependent planning problems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
A comparison of structural CSP decomposition methods
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Local search algorithms for partial MAXSAT
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Hi-index | 0.00 |
For many constraint satisfaction problems, finding complete solutions is impossible (i.e. problems may be over-constrained). In such cases, we want a partial solution that satisfies as many constraints as possible. Several backtracking and local search algorithms exist that are based on the assignment of values to variables in a fixed order, until a complete solution or a reasonably good partial solution is obtained. In this study, we examine the dual graph approach for solving CSPs. The idea of dual graphs can be naturally extended to another structure-driven approach to CSPs, constraint directed backtracking that inherently handles k-ary constraints. In this paper, we present a constraint directed branch and bound (CDBB) algorithm to address the problem of over-constrained-ness. The algorithm constructs solutions of higher arity by joining solutions of lower arity. When computational resources are bounded, the algorithm can return partial solutions in an anytime fashion. Some interesting characteristics of the proposed algorithm are discussed. The algorithm is implemented and tested on a set of randomly generated problems. Our experimental results demonstrate that the CDBB consistently finds better solutions more quickly than backtracking with branch and bound. Our algorithm can be extended with intelligent backtracking schemes and local consistency maintenance mechanisms just like backtracking has been in the past.