Qualitative multi-criteria preference representation and reasoning

The research reported on in this thesis is part of a larger research project that aims to develop a negotiation support system called the Pocket Negotiator. This thesis focuses on the question how such a system can represent and reason about a user’s preferences between the possible outcomes of a negotiation. In real-world negotiations, there are many negotiation issues which can have many different values, resulting in a large space of complex outcomes. A negotiation support system needs to have a model of the user’s preferences over this outcome space. Although most current negotiation support systems use numerical measures such as utility to represent preferences, such quantitative preferences are hard to specify for human users, and so it would be more natural to model the user’s preferences in a qualitative way. Moreover, due to the exponential size of the outcome space, it is not feasible to specify a preference ordering directly. Therefore, we aim to represent the preferences in a more compact way by aggregating multiple evaluation criteria that influence preference. The main research objective of this thesis is to develop a framework for the representation of, and reasoning about such qualitative multi-criteria preferences. The thesis makes the following contributions. \u95 We propose strategies to derive preferences from incomplete or uncertain information about the objects to be compared. The decisive and safe strategy for incomplete information is based on the notion of least and most preferred completions of objects. The strategies for uncertain information are based on an ordinal representation of the certainty levels of facts. \u95 We argue that instead of negotiation issues, the negotiators’ underlying interests should be chosen as criteria, especially if the issues are not preferentially independent. We show that the use of interests as criteria is more flexible than modelling conditional preferences, and provides a better explanation of the derived preferences. \u95 We present a general framework for the representation of qualitative, multicriteria preferences, called Qualitative Preference Systems (QPS). The framework defines outcomes as value assignments to a set of variables which can have arbitrary domains, includes a knowledge base that can impose (hard) constraints and define new (abstract) concepts, and defines three types of criteria that can be combined in a tree structure. We show that the QPS framework is expressive, as it can model conditional preferences and underlying interests, goal-based preferences, bipolar preferences, and preferences represented in two other well-known approaches that are representative for a large number of purely qualitative preference modelling approaches. Moreover, we show that the goal-based variant of QPS is just as expressive. \u95 For all proposed preference representation frameworks we define corresponding argumentation frameworks that include a logical language, a set of inference rules, and a defeat relation. Some of the argumentation frameworks also provide the possibility to reason with background knowledge to derive information about the values of variables by default. \u95 We propose a mechanism to generate explanations for preferences represented in a QPS. We use the intuition that preferences can be explained by the criteria that are deciding in the overall preference. Moreover, we show how a system can use user-provided explanations to update its current preference model. \u95 Finally, we introduce a modal logic, called Multi-Attribute Preference Logic (MPL), that provides a language for expressing several strategies to qualitatively derive a preference between objects from property rankings. Three such strategies from the literature on prioritized goals are modelled. The additional value of the logic is that it is possible to reason not only about which objects are preferred according to a certain ordering, but also about the relation between different orderings.Interactive IntelligenceElectrical Engineering, Mathematics and Computer Scienc