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Wiley InterScience | ||||||||||||||
  Computational IntelligenceVolume 20 Issue 2, Pages 137 - 157 Published Online: 20 Apr 2004 © 2010 Wiley Periodicals Inc.
Abstract | References | Full Text: PDF (Size: 214K) | Related Articles | Citation Tracking  Preference-Based Constrained Optimization with CP-Nets Copyright 2004 Blackwell Publishing, Inc. KEYWORDS preference • optimization • constraints • graphical models • CP-networks • configuration ABSTRACTMany 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. | 
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