Conference Paper

An Argumentation-Based Approach to Multiple Criteria Decision

DOI: 10.1007/11518655_24 Conference: Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 8th European Conference, ECSQARU 2005, Barcelona, Spain, July 6-8, 2005, Proceedings
Source: DBLP


The paper presents a first tentative work that investigates the interest and the questions raised by the introduction of argumenta- tion capabilities in multiple criteria decision-making. Emphasizing the positive and the negative aspects of possible choices, by means of ar- guments in favor or against them is valuable to the user of a decision- support system. In agreement with the symbolic character of arguments, the proposed approach remains qualitative in nature and uses a bipolar scale for the assessment of criteria. The paper formalises a multicriteria decision problem within a logical argumentation system. An illustrative example is provided. Various decision principles are considered, whose psychological validity is assessed by an experimental study.

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    • "no contradiction, no negotiation) argumentative discourses, designed to help a user to reach a goal, making the best decisions (see e.g. [3], [4]). This type of discourse contains a number of facets, which are all associated in a way to argumentation. "
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    ABSTRACT: Instructional texts consist of sequences of instruc-tions designed in order to reach an objective. The author or the generator of instructional texts must follow a number of principles to guarantee that the text is of any use. Similarly, a user must follow step by step the instructions in order to reach the results expected. In this paper, we explore facets of instructional texts: general prototypical structures, rhetorical structure and natural argumentation. Our study is based on an extensive corpus study with the aim of generating such texts.
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    • "The quality of an action is determined by how well its consequences satisfy certain criteria. For example , (Amgoud et al., 2005) present an approach in which arguments of various strengths in favour of and against a decision are compared. However, it is a twostep process in which argumentation is used only for epistemic reasoning. "
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    ABSTRACT: In the context of practical reasoning, such as decision making and negotiation, 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.
    ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence, Volume 1 - Artificial Intelligence, Rome, Italy, January 28-30, 2011; 01/2011
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    • "Bipolarity is an important topic in several domains, e.g., psychology (Tversky and Kahneman, 1992; C. E. Osgood and Tannenbaum, 1957; J. T. Cacioppo and Berntson, 1997), multi-criteria decision making (Grabisch and Labreuche, 2005), and more recently in AI (argumentation (L. Amgoud and Prade, 2005) and qualitative reasoning (Benferhat et al., 2002, 2006; Dubois and Fargier, 2005, 2006)). Preferences on a set of possible choices are often expressed in two forms: positive and negative statements. "
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    ABSTRACT: Real-life problems present several kinds of preferences. We focus on problems with both positive and negative preferences, that we call bipolar preference problems . Although seem- ingly specular notions, these two kinds of preferences should be dealt with differently to obtain the desired natural behaviour. We technically address this by generalizing the soft constraint formalism, which is able to model problems with one kind of preferences. We show that soft constraints model only negative preferences, and we add to them a new math- ematical structure which allows to handle positive preferences as well. We also address the issue of the compensation between positive and negative preferences, studying the proper- ties of this operation. Finally, we extend the notion of arc c onsistency to bipolar problems, and we show how branch and bound (with or without constraint propagation) can be easily adapted to solve such problems.
    Journal of Experimental & Theoretical Artificial Intelligence 06/2010; 22(2):135-158. DOI:10.1080/09528130903010212 · 1.00 Impact Factor
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