Classical Decision Theory provides a normative framework for representing and reasoning about complex preferences. Straightforward application of this theory to automate decision making is difficult due to high elicitation cost. In response to this problem, researchers have recently developed a number of qualitative, logic-oriented approaches for representing and reasoning about references. While effectively addressing some expressiveness issues, these logics have not proven powerful enough for building practical automated decision making systems. In this paper we present a hybrid approach to preference elicitation and decision making that is grounded in classical multi-attribute utility theory, but can make effective use of the expressive power of qualitative approaches. Specifically, assuming a partially specified multilinear utility function, we show how comparative statements about classes of decision alternatives can be used to further constrain the utility function and thus identify sup-optimal alternatives. This work demonstrates that quantitative and qualitative approaches can be synergistically integrated to provide effective and flexible decision support.
Preference-Based Planning in Stochastic Environments: From Partially-Ordered Temporal Goals to Most Preferred Policies
Missouri State University（密苏里州立大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Florida（佛罗里达大学）；Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究所）
Dynamic Detection of Relevant Objectives and Adaptation to Preference Drifts in Interactive Evolutionary Multi-Objective Optimization
Centre for Logistics & Sustainability Analytics, Kent Business School, University of Kent（肯特大学肯特商学院物流与可持续性分析中心）；Université de Haute-Alsace（上阿尔萨斯大学）；Alliance Manchester Business School, University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
