Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Tree clustering for constraint networks (research note)
Artificial Intelligence
Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
Constraint satisfaction using constraint logic programming
Artificial Intelligence - Special volume on constraint-based reasoning
The hardest constraint problems: a double phase transition
Artificial Intelligence
Easy problems are sometimes hard
Artificial Intelligence
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
Representation Selection for Constraint Satisfaction: A Case Study Using n-Queens
IEEE Expert: Intelligent Systems and Their Applications
Applications of Constraint Programming
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
The CHIP System and Its Applications
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Retractable contract network for empowerment in workforce scheduling
Multiagent and Grid Systems - Negotiation and Scheduling Mechanisms for Multiagent Systems
Dual modelling of permutation and injection problems
Journal of Artificial Intelligence Research
The rules of constraint modelling
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Transforming and refining abstract constraint specifications
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
Using CBR to select solution strategies in constraint programming
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Much research effort has been applied to finding effective ways for solving constraint satisfaction problems. However, the most fundamental aspect of constraint satisfaction problem solving, problem formulation, has received much less attention. This is important because the selection of an appropriate formulation can have dramatic effects on the efficiency of any constraint satisfaction problem solving algorithm.In this paper, we address the issue of problem formulation. We identify the heuristic nature of generating a good formulation and we propose a context for this process. Our work presents the research community with a focus for the many elements which affect problem formulation and this is illustrated with the example adding redundant constraints. It also provides a significant step towards the goal of automatic selection of problem formulations.