In this section
@article{Toni:2023:10.1080/11663081.2023.2244716,
author = {Toni, F and Rago, A and Cyras, K},
doi = {10.1080/11663081.2023.2244716},
journal = {Journal of Applied Non Classical Logics},
pages = {224--243},
title = {Forecasting with jury-based probabilistic argumentation},
url = {http://dx.doi.org/10.1080/11663081.2023.2244716},
volume = {33},
year = {2023}
}
TY - JOUR
AB - Probabilistic Argumentation supports a form of hybrid reasoning by integratingquantitative (probabilistic) reasoning and qualitative argumentation in a naturalway. Jury-based Probabilistic Argumentation supports the combination of opinionsby different reasoners. In this paper we show how Jury-based Probabilistic Abstract Argumentation (JPAA) and a form of Jury-based Probabilistic Assumptionbased Argumentation (JPABA) can naturally support forecasting, whereby subjective probability estimates are combined to make predictions about future occurrences of events. The form of JPABA we consider is an instance of JPAA andresults from integrating Assumption-Based Argumentation (ABA) and probabilityspaces expressed by Bayesian networks, under the so-called constellation approach.It keeps the underlying structured argumentation and probabilistic reasoning modules separate while integrating them. We show how JPAA and (the considered formof) JPABA can be used to support forecasting by 1) supporting different forecasters (jurors) to determine the probability of arguments (and, in the JPABA case,sentences) with respect to their own probability spaces, while sharing arguments(and their components); and 2) supporting the aggregation of individual forecaststo produce group forecasts.
AU - Toni,F
AU - Rago,A
AU - Cyras,K
DO - 10.1080/11663081.2023.2244716
EP - 243
PY - 2023///
SN - 1166-3081
SP - 224
TI - Forecasting with jury-based probabilistic argumentation
T2 - Journal of Applied Non Classical Logics
UR - http://dx.doi.org/10.1080/11663081.2023.2244716
UR - http://hdl.handle.net/10044/1/105331
VL - 33
ER -
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