Argumentation-Based Preference Modelling with Incomplete Information

Abstract

Abstract. 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 argumentationbased framework for the modelling of and automated reasoning about multi-issue 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.

An Argumentation Framework for Deriving Qualitative Risk Sensitive Preferences

“…The argumentation framework we present here builds on the framework introduced in [1]. An abstract argumentation framework (AF) [9] is a pair A, → where A is a set of arguments, and → a binary defeat relation (informally, a counterargument relation) on A.…”

“…In [1], an argumentation framework for reasoning about qualitative preferences is presented. It introduces a safe and decisive strategy to deal with incomplete information.…”

“…Two objects are equally preferred if they have the same number of attributes on every importance level. The argumentation framework of [1] models the lexicographic ordering. In this paper we extend that framework to incorporate defeasible knowledge.…”

“…First, it can be the case that nothing can be derived about the truth of an attribute for a certain object. This situation is treated in [1] and the current framework can be extended in a similar way as presented there. Second, it can happen that it can both be (defeasibly) derived that an object has an attribute and that it does not, so we have contradictory information.…”