The consistent labeling problem and its algorithm: toward exact-case complexities and theory-based heuristics
Constraint satisfaction algorithms
Computational Intelligence
Performance measurement and analysis of certain search algorithms.
Performance measurement and analysis of certain search algorithms.
A polynomial time algorithm for the N-Queens problem
ACM SIGART Bulletin
Different perspectives of the N-Queens problem
CSC '92 Proceedings of the 1992 ACM annual conference on Communications
A Glimpse of Constraint Satisfaction
Artificial Intelligence Review
Binary vs. non-binary constraints
Artificial Intelligence
Constraint Programming Lessons Learned from Crossword Puzzles
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Modular lazy search for Constraint Satisfaction Problems
Journal of Functional Programming
Dual modelling of permutation and injection problems
Journal of Artificial Intelligence Research
Reformulating constraint satisfaction problems to improve scalability
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Reformulating CSPs for scalability with application to geospatial reasoning
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
The complexity of constraint satisfaction in prolog
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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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Representation selection for a constraint satisfaction problem (CSP) is addressed. The CSP problem class is introduced and the classic n-queens problem is used to show that many different CSP representations may exist for a given real-world problem. The complexities of solving these alternative representations are compared empirically and theoretically. The good agreement found is due to two key features of the analytic results, their generality and their precision (or instance specificity), which are also discussed. The n-queens problem is used only to provide a convenient case study; the approach to CSP representation selection applies to arbitrary problems that can be formulated in terms of CSP and, when the corresponding mathematical results are available, should also be readily applicable when selecting representations in domains other than CSP.