Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison
<|reference_start|>Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison: Many logic programming based approaches can be used to describe and solve combinatorial search problems. On the one hand there is constraint logic programming which computes a solution as an answer substitution to a query containing the variables of the constraint satisfaction problem. On the other hand there are systems based on stable model semantics, abductive systems, and first order logic model generators which compute solutions as models of some theory. This paper compares these different approaches from the point of view of knowledge representation (how declarative are the programs) and from the point of view of performance (how good are they at solving typical problems).<|reference_end|>
@article{pelov2000logic,
    title={Logic Programming Approaches for Representing and Solving Constraint
  Satisfaction Problems: A Comparison},
    author={Nikolay Pelov, Emmanuel De Mot, Marc Denecker},
    journal={LPAR 2000, Lecture Notes in Artificial Intelligence, vol. 1955,
  Springer, 2000, pp. 225-239},
    year={2000},
    archivePrefix={arXiv},
    eprint={cs/0011030},
    primaryClass={cs.AI}
}