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Polberg, S., & Doder, D. (2014). Probabilistic Abstract Dialectical Frameworks. In E. Fermé & J. Leite (Eds.), Logics in Artificial Intelligence (pp. 591–599). Springer. https://doi.org/10.1007/978-3-319-11558-0_42
E192-02 - Forschungsbereich Databases and Artificial Intelligence
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Published in:
Logics in Artificial Intelligence
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Date (published):
2014
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Event name:
European Conference on Logics in Artificial Intelligence (JELIA)
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Event date:
13-Sep-2006 - 15-Sep-2006
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Event place:
Liverpool, UK, EU
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Number of Pages:
9
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Publisher:
Springer, 8761
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Peer reviewed:
Yes
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Abstract:
Although Dung´s frameworks are widely approved tools for abstract argumentation, their abstractness makes expressing notions such as support or uncertainty very difficult. Thus, many of their generalizations were created, including the probabilistic argumentation frameworks (PrAFs) and the abstract dialectical frameworks (ADFs). While the first allow modeling uncertain arguments and attacks, the l...
Although Dung´s frameworks are widely approved tools for abstract argumentation, their abstractness makes expressing notions such as support or uncertainty very difficult. Thus, many of their generalizations were created, including the probabilistic argumentation frameworks (PrAFs) and the abstract dialectical frameworks (ADFs). While the first allow modeling uncertain arguments and attacks, the latter can handle various dependencies between arguments. Although the actual probability layer in PrAFs is independent of the chosen semantics, new relations pose new challenges and new interpretations of what is the probability of a relation. Thus, the methodology for handling uncertainties cannot be shifted to more general structures without any further thought. In this paper we show how ADFs are extended with probabilities.
