Syntactic reasoning with conditional probabilities in deductive argumentation

Hunter, Anthony and Potyka, Nico 2023. Syntactic reasoning with conditional probabilities in deductive argumentation. Artificial Intelligence 321 , 103934. 10.1016/j.artint.2023.103934

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Abstract

Evidence from studies, such as in science or medicine, often corresponds to conditional probability statements. Furthermore, evidence can conflict, in particular when coming from multiple studies. Whilst it is natural to make sense of such evidence using arguments, there is a lack of a systematic formalism for representing and reasoning with conditional probability statements in computational argumentation. We address this shortcoming by providing a formalization of conditional probabilistic argumentation based on probabilistic conditional logic. We provide a semantics and a collection of comprehensible inference rules that give different insights into evidence. We show how arguments constructed from proofs and attacks between them can be analyzed as arguments graphs using dialectical semantics and via the epistemic approach to probabilistic argumentation. Our approach allows for a transparent and systematic way of handling uncertainty that often arises in evidence.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0004-3702
Date of First Compliant Deposit:	19 October 2023
Date of Acceptance:	18 April 2023
Last Modified:	30 Oct 2023 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/163311

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