A formal two-phase model of democratic policy deliberation is presented, in which in the first phase sufficient and necessary criteria for proposals to be accepted are determined (the 'acceptable' criteria) and in the second phase proposals are made and evaluated in light of the acceptable criteria resulting from the first phase. Such a separation gives the discussion a clear structure and prevents time and resources from being wasted on evaluating arguments for proposals based on unacceptable criteria. Argument schemes for both phases are defined and formalised in a logical framework for structured argumentation. The process of deliberation is abstracted from and it is assumed that both deliberation phases result in a set of arguments and attack and defeat relations between them. The acceptability status of criteria and proposals within the resulting argumentation framework is then evaluated using preferred semantics. For cases where preferences are required to choose between proposals, inference rules for deriving preferences between sets from an ordering of their elements are given.

This paper presents an argumentation-based framework for the modelling of, and automated reasoning about multi-attribute preferences of a qualitative nature. The framework presents preferences according to the lexicographic ordering that is well-understood by humans. Preferences are derived in part from knowledge. Knowledge, however, may be incomplete or uncertain. The main contribution of the paper is that it shows how to reason about preferences when only incomplete or uncertain information is available. We propose a strategy that allows reasoning with incomplete information and discuss a number of strategies to handle uncertain information. It is shown how to extend the basic framework for modelling preferences to incorporate these strategies.

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.

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 criterion's 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.

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