Abstract
Traditional constraint logic programming (CLP) specifies an optimization problem by using a set of constraints and an objective function. In many applications, optimal solutions may be difficult or impossible to obtain, and hence we are interested in finding suboptimal solutions, by either relaxing the constraints or the objective function. Hierarchical constraint logic programming (HCLP) [1] is such a strategy by extending CLP to support required as well as relaxable constraints. HCLP proposed preferences on constraints indicating the relative importance of constraints and organizing them into a hierarchy. Essentially, the solutions of interest must satisfy the required constraints but need not satisfy the relaxable constraints. HCLP has proven to be useful tool in solving the over-constrained applications.
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References
Wilson, M., Borning, A.: Hierarchical Constraint Logic Programming. Journal of Logic Programming 16, 277–318 (1993)
Govindarajan, K., Jayaraman, B., Mantha, S.: Preference Logic Programming. In: International Conference on Logic Programming (ICLP), pp. 731–745 (1995)
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© 2006 Springer-Verlag Berlin Heidelberg
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Guo, HF., Liu, M., Jayaraman, B. (2006). Relaxation on Optimization Predicates. In: Etalle, S., Truszczyński, M. (eds) Logic Programming. ICLP 2006. Lecture Notes in Computer Science, vol 4079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11799573_33
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DOI: https://doi.org/10.1007/11799573_33
Publisher Name: Springer, Berlin, Heidelberg
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