Back to Search
Start Over
Preference-Based Constrained Optimization with CP-Nets.
- Source :
- Computational Intelligence; May2004, Vol. 20 Issue 2, p137-157, 21p, 5 Diagrams
- Publication Year :
- 2004
-
Abstract
- Many artificial intelligence (AI) tasks, such as product configuration, decision support, and the construction of autonomous agents, involve a process of constrained optimization, that is, optimization of behavior or choices subject to given constraints. In this paper we present an approach for constrained optimization based on a set of hard constraints and a preference ordering represented using a CP-network—a graphical model for representing qualitative preference information. This approach offers both pragmatic and computational advantages. First, it provides a convenient and intuitive tool for specifying the problem, and in particular, the decision maker's preferences. Second, it admits an algorithm for finding the most preferred feasible (Pareto-optimal) outcomes that has the following anytime property: the set of preferred feasible outcomes are enumerated without backtracking. In particular, the first feasible solution generated by this algorithm is Pareto optimal. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08247935
- Volume :
- 20
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Computational Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 12862364
- Full Text :
- https://doi.org/10.1111/j.0824-7935.2004.00234.x