A Survey of Uncertainties and Their Consequences in Probabilistic Legal Argumentation

Matthias Grabmair, Kevin D. Ashley

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this chapter, we present a survey of different sorts of uncertainties lawyers reason with and connect them to the issue of how probabilistic models of argumentation can facilitate litigation planning. We briefly survey Bayesian Networks as a representation for argumentation in the context of a realistic example. After introducing the Carneades argument model and its probabilistic semantics, we propose an extension to the Carneades Bayesian Network model to support probability distributions over argument weights, a feature we believe is desirable. Finally, we scout possible future approaches to facilitate reasoning with argument weights.

Original languageEnglish
Title of host publicationSynthese Library
PublisherSpringer Science and Business Media B.V.
Pages61-85
Number of pages25
DOIs
StatePublished - 2013
Externally publishedYes

Publication series

NameSynthese Library
Volume362
ISSN (Print)0166-6991
ISSN (Electronic)2542-8292

Keywords

  • Bayesian Network
  • Conditional Probability Table
  • Probabilistic Graphical Model
  • Trade Secret
  • Unique Product

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