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A Bayesian approach to forward and inverse abstract argumentation problems

A Bayesian approach to forward and inverse abstract argumentation problems This paper studies a fundamental mechanism by which conflicts between arguments are drawn from sentiments regarding acceptability of the arguments. Given sets of arguments, an inverse abstract argumentation problem seeks attack relations between arguments such that acceptability semantics interprets each argument in the sets of arguments as being acceptable in each of the attack relations. It is an inverse problem of the traditional problem we refer to as the forward abstract argumentation problem. Given an attack relation, the forward abstract argumentation problem seeks sets of arguments such that acceptability semantics interprets each argument in the sets of arguments as being acceptable in the attack relation. We give a probabilistic model of argumentation-theoretic inference. It is a generative model formalising the process by which acceptability semantics interprets acceptability of arguments in a given attack relation. We show that it gives a broad view of solutions to the forward and inverse abstract argumentation problems. Specifically, solutions to the inverse and forward abstract argumentation problems are shown to be equivalent to a maximum likelihood estimate and maximum likelihood prediction, respectively, which are both available with the generative model. In addition, they are shown to be special cases of the posterior distribution and the evidence, respectively, which are both obtained by probabilistic inference on the generative model. We report an experiment result and application example of the generative model in the inverse problems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Non-Classical Logics Taylor & Francis

A Bayesian approach to forward and inverse abstract argumentation problems

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Publisher
Taylor & Francis
Copyright
© 2022 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1958-5780
eISSN
1166-3081
DOI
10.1080/11663081.2022.2144830
Publisher site
See Article on Publisher Site

Abstract

This paper studies a fundamental mechanism by which conflicts between arguments are drawn from sentiments regarding acceptability of the arguments. Given sets of arguments, an inverse abstract argumentation problem seeks attack relations between arguments such that acceptability semantics interprets each argument in the sets of arguments as being acceptable in each of the attack relations. It is an inverse problem of the traditional problem we refer to as the forward abstract argumentation problem. Given an attack relation, the forward abstract argumentation problem seeks sets of arguments such that acceptability semantics interprets each argument in the sets of arguments as being acceptable in the attack relation. We give a probabilistic model of argumentation-theoretic inference. It is a generative model formalising the process by which acceptability semantics interprets acceptability of arguments in a given attack relation. We show that it gives a broad view of solutions to the forward and inverse abstract argumentation problems. Specifically, solutions to the inverse and forward abstract argumentation problems are shown to be equivalent to a maximum likelihood estimate and maximum likelihood prediction, respectively, which are both available with the generative model. In addition, they are shown to be special cases of the posterior distribution and the evidence, respectively, which are both obtained by probabilistic inference on the generative model. We report an experiment result and application example of the generative model in the inverse problems.

Journal

Journal of Applied Non-Classical Logics – Taylor & Francis

Published: Oct 2, 2022

Keywords: Abstract argumentation frameworks; acceptability semantics; inverse problems; generative models; Bayesian statistics; machine learning

References

  • Scc-recursiveness: a general schema for argumentation semantics
  • Argumentation in artificial intelligence
  • A natural language bipolar argumentation approach to support users in online debate interactions
  • On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming, and n-person games
  • Characteristics of multiple viewpoints in abstract argumentation
  • Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
  • Argumentation mining: State of the art and emerging trends
  • An application of formal argumentation: Fusing bayesian networks in multi-agent systems
  • Synthesizing argumentation frameworks from examples
  • On searching explanatory argumentation graphs