Belief structures, possibility theory and decomposable confidence measures on finite sets
Computers and Artificial Intelligence
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Using arguments for making decisions: a possibilistic logic approach
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A unified framework for order-of-magnitude confidence relations
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Defining relative likelihood in partially-ordered preferential structures
Journal of Artificial Intelligence Research
Arguing over Actions That Involve Multiple Criteria: A Critical Review
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Comparing sets of positive and negative arguments: Empirical assessment of seven qualitative rules
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
On the qualitative comparison of decisions having positive and negative features
Journal of Artificial Intelligence Research
Uncertainty in bipolar preference problems
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Bipolar preference problems: framework, properties and solving techniques
CSCLP'06 Proceedings of the constraint solving and contraint logic programming 11th annual ERCIM international conference on Recent advances in constraints
An argumentation-based approach to multiple criteria decision
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Decisions can be assessed by sets of positive and negative arguments — the problem is then to compare these sets. Studies in psychology have shown that the scale of evaluation of decisions should then be considered as bipolar. The second characteristic of the problem we are interested in is the qualitative nature of the decision process — decisions are often made on the basis of an ordinal ranking of the arguments rather than on a genuine numerical evaluation of their degrees of attractiveness or rejection. In this paper, we present and axiomatically characterize two methods based on possibilistic order of magnitude reasoning that are capable of handling positive and negative affects. They are extensions of the maximin and maximax criteria to the bipolar case. More decisive rules are also proposed, capturing both the Pareto principle and the idea of order of magnitude reasoning.