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A model-based theorem prover for epistemic graphs for argumentation. (English) Zbl 07170122

Kern-Isberner, Gabriele (ed.) et al., Symbolic and quantitative approaches to reasoning with uncertainty. 15th European conference, ECSQARU 2019, Belgrade, Serbia, September 18–20, 2019. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 11726, 50-61 (2019).
Summary: Epistemic graphs are a recent proposal for probabilistic argumentation that allows for modelling an agent’s degree of belief in an argument and how belief in one argument may influence the belief in other arguments. These beliefs are represented by probability distributions and how they affect each other is represented by logical constraints on these distributions. Within the full language of epistemic constraints, we distinguish a restricted class which offers computational benefits while still being powerful enough to allow for handling of many other argumentation formalisms and that can be used in applications that, for instance, rely on Likert scales. In this paper, we propose a model-based theorem prover for reasoning with the restricted epistemic language.
For the entire collection see [Zbl 1419.68012].

MSC:

68T37 Reasoning under uncertainty in the context of artificial intelligence

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