Conditional Preference Networks with User's Genuine Decisions
computational intelligence(2020)
摘要
User's choices involve habitual behavior and genuine decision. Habitual behavior is often expressed using preferences. In a multiattribute case, theConditional Preference Network(CP-net) is a graphical model to represent user's conditionalceteris paribus(all else being equal) preference statements. Indeed, the CP-net induces a strict partial order over the outcomes. By contrast, we argue that genuine decisions are environmentally influenced and introduce the notion of "comfort" to represent this type of choices. In this article, we propose an extension of the CP-net model that we call theCP-net with Comfort(CPC-net) to represent a user's comfort with preferences. Given that preference and comfort might be two conflicting objectives, we define the Pareto optimality of outcomes when achieving outcome optimization with respect to a given CPC-net. Then, we propose a backtrack search algorithm to find the Pareto optimal outcomes. On the other hand, two outcomes can stand in one of six possible relations with respect to a CPC-net. The exact relation can be obtained by performing dominance testing in the corresponding CP-net and comparing the numeric comforts.
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关键词
backtrack search,CP-net,Pareto optimality,preferences
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