Research Note on Uncertain Probabilities and Abstract Argumentation
원문 초록
The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1.5C (medium confidence)." Such reports directly feed the public discourse, but nuances such as the degree of belief and of confidence are often lost. In this paper, we propose a formal account for allowing such degrees of belief and the associated confidence to be used to label arguments in abstract argumentation settings. Differently from other proposals in probabilistic argumentation, we focus on the task of probabilistic inference over a chosen query building upon Sato's distribution semantics which has been already shown to encompass a variety of cases including the semantics of Bayesian networks. Borrowing from the vast literature on such semantics, we examine how such tasks can be dealt with in practice when considering uncertain probabilities, and discuss the connections with existing proposals for probabilistic argumentation.
함께 살펴볼 논문
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Neuro-Argumentative Learning with Case-Based Reasoning
On the Structure of Game Provenance and its Applications
A Formalisation of Abstract Argumentation in Higher-Order Logic
Deep Learning for Abstract Argumentation Semantics
MisGAN: Learning from Incomplete Data with Generative Adversarial Networks
An argumentative agent-based model of scientific inquiry
토론
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