Heuristic reasoning about uncertainty: an artificial intelligence approach
Heuristic reasoning about uncertainty: an artificial intelligence approach
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Uncertainty and vagueness in knowledge based systems
Uncertainty and vagueness in knowledge based systems
PULCinella: a general tool for propagating uncertainty in valuation networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Why do we need foundations for modelling uncertainties?
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Artificial Intelligence
Arguments, contradictions and practical reasoning
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
ECSQAU Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Making inconsistency respectable: a logical framework for inconsistency in reasoning
FAIR '91 Proceedings of the International Workshop on Fundamentals of Artificial Intelligence Research
The Development of a "Logic of Argumentation"
IPMU '92 Proceedings of the 4th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems: Advanced Methods in Artificial Intelligence
Updating with belief functions, ordinal conditional functions and possibility measures
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Dominant decisions by argumentation agents
ArgMAS'09 Proceedings of the 6th international conference on Argumentation in Multi-Agent Systems
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Argumentation is the process of constructing arguments about propositions, and the assignment of statements of confidence to those propositions based on the nature and relative strength of their supporting arguments. The process is modelled as a labelled deductive system, in which propositions are doubly labelled with the grounds on which they are based and a representation of the confidence attached to the argument. Argument construction is captured by a generalised argument consequence relation based on the ∧, →-fragment of minimal logic. Arguments can be aggregated by a variety of numeric and symboric flattening functions. This approach appears to shed light on the common logical structure of a variety of quantitative, qualitative and defeasible uncertainty calculi.