ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
In search of the best constraint satisfaction search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Experimental results on the crossover point in random 3-SAT
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Representation Selection for Constraint Satisfaction: A Case Study Using n-Queens
IEEE Expert: Intelligent Systems and Their Applications
Using Auxiliary Variables and Implied Constraints to Model Non-Binary Problems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Search lessons learned from crossword puzzles
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Solving Non-binary CSPs Using the Hidden Variable Encoding
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Optimization of Simple Tabular Reduction for Table Constraints
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Reformulating Positive Table Constraints Using Functional Dependencies
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Crossword Puzzles as a Constraint Problem
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
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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Constraint programming is a methodology for solving difficult combinatorial problems. In the methodology, one makes three design decisions: the constraint model, the search algorithm for solving the model, and the heuristic for guiding the search. Previous work has shown that the three design decisions can greatly influence the efficiency of a constraint programming approach. However, what has not been explicitly addressed in previous work is to what level, if any, the three design decisions can be made independently. In this paper we use crossword puzzle generation as a case study to examine this question. We draw the following general lessons from our study. First, that the three design decisions--model, algorithm, and heuristic--are mutually dependent. As a consequence, in order to solve a problem using constraint programming most efficiently, one must exhaustively explore the space of possible models, algorithms, and heuristics. Second, that if we do assume some form of independence when making our decisions, the resulting decisions can be sub-optimal by orders of magnitude.