User's choices involve habitual behavior and genuine decision. Habitual behavior is often expressed using preferences. In a multiattribute case, the Conditional Preference Network (CP‐net) is a graphical model to represent user's conditional ceteris 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 the CP‐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.

中文翻译：

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具有用户真实决策的条件偏好网络

用户的选择涉及习惯行为和真正的决定。习惯行为通常通过偏好来表达。在多属性情况下，条件优先级网络（CP-net）是一个图形模型，用于表示用户的条件性paribus（在其他所有条件相同的情况下）的优先级声明。确实，CP-net在结果上产生了严格的部分顺序。相比之下，我们认为真正的决定会受到环境的影响，并引入“舒适”这一概念来代表这种选择。在本文中，我们提出了CP-net模型的扩展，我们称其为带有Comfort的CP-net。（CPC-net）代表用户对首选项的舒适感。鉴于偏好和舒适度可能是两个相互矛盾的目标，因此当针对给定的CPC网络实现结果优化时，我们定义了结果的帕累托最优。然后，我们提出了一种回溯搜索算法来找到帕累托最优结果。另一方面，相对于CPC网络，两个结果可以处于六个可能的关系之一。可以通过在相应的CP-net中执行优势测试并比较数值舒适度来获得精确的关系。