Syntactic reasoning with conditional probabilities in deductive argumentation

卷:321

Article

关键词: BAYESIAN NETWORK;    LOGIC;    INCONSISTENCY;    SEMANTICS;    SYSTEMS;    PROOF;    GOALS;   

DOI  :  10.1016/j.artint.2023.103934

来源: SCIE

【 摘 要 】

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.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

【 授权许可】

Free