Wietske Visser

An argumentation framework for qualitative multi-criteria preferences

Preferences between different alternatives (products, decisions, agreements etc.) are often based on multiple criteria. Qualitative Preference Systems (QPS) is a formal framework for the representation of qualitative multi-criteria preferences in which a criterions preference is defined based on the values of attributes or by combining multiple subcriteria in a cardinality-based or lexicographic way. In this paper we present a language and reasoning mechanism to represent and reason about such qualitative multi-criteria preferences. We take an argumentation-based approach and show that the presented argumentation framework correctly models a QPS. Then we extend this argumentation framework in such a way that it can derive missing information from background knowledge, which makes it more flexible in case of incomplete specifications.

Multi-angle view on preference elicitation for negotiation support systems

Motivation -- Elicitation of preferences is crucial in negotiation support. This is a non-trivial task which could be supported by computers. Research approach -- Experiment in which 32 participants have to order holidays using different preference elicitation techniques including a navigational task and affective scoring. The results were used as input for a lexicographic ordering algorithm. Findings/design -- Traditional property rating approach seems most preferred by the participants and resulted in one of the best orderings of the outcomes space to match their preferences, at least when using the lexicographic algorithm. Originality/value -- The elicitation process is approached from an algorithmic perspective as well as from a user-centred perspective for both navigation and affective attitude. Take away message -- A multi-angle approach gives a richer understanding of the process of preference elicitation.

Argumentation-based preference modelling with incomplete information

No intelligent decision support system functions even remotely without knowing the preferences of the user. A major problem is that the way average users think about and formulate their preferences does not match the utility-based quantitative frameworks currently used in decision support systems. For the average user qualitative models are a better fit. This paper presents an argumentation-based framework for the modelling of, and automated reasoning about multiissue preferences of a qualitative nature. The framework presents preferences according to the lexicographic ordering that is well-understood by humans. The main contribution of the paper is that it shows how to reason about preferences when only incomplete information is available. An adequate strategy is proposed that allows reasoning with incomplete information and it is shown how to incorporate this strategy into the argumentation-based framework for modelling preferences.

Reasoning about Interest-Based Preferences

In decision making, negotiation, and other kinds of practical reasoning, it is necessary to model preferences over possible outcomes. Such preferences usually depend on multiple criteria. We argue that the criteria by which outcomes are evaluated should be the satisfaction of a person’s underlying interests: the more an outcome satisfies his interests, the more preferred it is. Underlying interests can explain and eliminate conditional preferences. Also, modelling interests will create a better model of human preferences, and can lead to better, more creative deals in negotiation. We present an argumentation framework for reasoning about interest-based preferences. We take a qualitative approach and provide the means to derive both ceteris paribus and lexicographic preferences.

Multi-attribute preference logic

Preferences for objects are commonly derived from ranked sets of properties or multiple attributes associated with these objects. There are several options or strategies to qualitatively derive a preference for one object over another from a property ranking. We introduce a modal logic, called multi-attribute preference logic, that provides a language for expressing such strategies. The logic provides the means to represent and reason about qualitative multi-attribute preferences and to derive object preferences from property rankings. The main result of the paper is a proof that various well-known preference orderings can be defined in multi-attribute preference logic.

Goal-Based Qualitative Preference Systems

Goals are not only used to identify desired states or outcomes, but may also be used to derive qualitative preferences between outcomes. We show that Qualitative Preference Systems (QPSs) provide a general, flexible and succinct way to represent preferences based on goals. If the domain is not Boolean, preferences are often based on orderings on the possible values of variables. We show that QPSs that are based on such multi-valued criteria can be translated into equivalent goal-based QPSs that are just as succinct. Finally, we show that goal-based QPSs allow for more fine-grained updates than their multi-valued counterparts. These results show that goals are very expressive as a representation of qualitative preferences and moreover, that there are certain advantages of using goals instead of multi-valued criteria.

An argumentation framework for deriving qualitative risk sensitive preferences

Preferences are derived in part from knowledge. Knowledge, however, may be defeasible. We present an argumentation framework for deriving qualitative, multi-attribute preferences and incorporate defeasible reasoning about knowledge. Intuitively, preferences based on defeasible conclusions are not as strong as preferences based on certain conclusions, since defeasible conclusions may turn out not to hold. This introduces risk when such knowledge is used in practical reasoning. Typically, a risk prone attitude will result in different preferences than a risk averse attitude. In this paper we introduce qualitative strategies for deriving risk sensitive preferences.