Journal ArticleDOI
Probabilistic Argumentation Systems and Abduction
TLDR
The concepts of complete and partial models are introduced with the goal to study the quality of inference procedures and the added value introduced by probability into model based diagnostics will be discussed.Abstract:
Probabilistic argumentation systems are based on assumption-based reasoning for obtaining arguments supporting hypotheses and on probability theory to compute probabilities of supports. Assumption-based reasoning is closely related to hypothetical reasoning or inference through theory formation. The latter approach has well known relations to abduction and default reasoning. In this paper assumption-based reasoning, as an alternative to theory formation aiming at a different goal, will be presented and its use for abduction and model-based diagnostics will be explained. Assumption-based reasoning is well suited for defining a probability structure on top of it. On the base of the relationships between assumption-based reasoning on the one hand and abduction on the other hand, the added value introduced by probability into model based diagnostics will be discussed. Furthermore, the concepts of complete and partial models are introduced with the goal to study the quality of inference procedures. In particular this will be used to compare abductive to possible explanations.read more
Citations
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Journal ArticleDOI
A Reasoning Model Based on the Production of Acceptable Arguments
Leila Amgoud,Claudette Cayrol +1 more
TL;DR: The argumentation framework proposed by Dung is refined by taking into account preference relations between arguments in order to integrate two complementary points of view on the concept of acceptability, which refines previous works by Prakken and Sartor.
Journal ArticleDOI
Graduality in argumentation
TL;DR: The purpose is to introduce "graduality" in the selection of the best arguments, i.e. to be able to partition the set of the arguments in more than the two usual subsets of "selected" and "non-selected" arguments in order to represent different levels of selection.
Book ChapterDOI
A Game-Theoretic Measure of Argument Strength for Abstract Argumentation
Paul-Amaury Matt,Francesca Toni +1 more
TL;DR: A measure of argument strength is formalised by applying the concept of value of a game, as defined in Game Theory (von Neumann 1928), which satisfies a number of intuitively appealing properties that can be derived mathematically from the minimax theorem.
Book
Argumentation Machines: New Frontiers in Argument and Computation
Chris Reed,Timothy J. Norman +1 more
TL;DR: The volume not only offers in-depth assessments of existing research, but also represents a substantial advance in the state of the art, and lays out a roadmap for future work in this newly emerging cross-disciplinary field.
Posted Content
Combining statistics and arguments to compute trust
TL;DR: It is proved that the method for constructing Dempster-Shafer belief functions by combining statistical information concerning the past behaviour of the target and arguments concerning the target's expected behaviour extends a standard computational method for trust that relies upon statistical information only.
References
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Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book
A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
Journal ArticleDOI
A theory of diagnosis from first principles
TL;DR: The theory accommodates diagnostic reasoning in a wide variety of practical settings, including digital and analogue circuits, medicine, and database updates, and reveals close connections between diagnostic reasoning and nonmonotonic reasoning.
Journal ArticleDOI
Probabilistic reasoning in intelligent systems: Networks of plausible inference
TL;DR: Probabilistic methods to create the areas, of computational tools, and apparently daphne koller and learning structures evidential reasoning, Pearl is a language for i've is not great give the best references.
Journal ArticleDOI
A logical framework for default reasoning
TL;DR: A simple logical framework for default reasoning by treating defaults as predefined possible hypotheses is presented, and it is shown how this idea subsumes the intuition behind Reiter's default logic.