Abstract
Probabilistic epistemic argumentation allows for reasoning about argumentation problems in a way that is well founded by probability theory. Epistemic states are represented by probability functions over possible worlds and can be adjusted to new beliefs using update operators. While the use of probability functions puts this approach on a solid foundational basis, it also causes computational challenges as the amount of data to process depends exponentially on the number of arguments. This leads to bottlenecks in applications such as modelling opponent’s beliefs for persuasion dialogues. We show how update operators over probability functions can be related to update operators over much more compact representations that allow polynomial-time updates. We discuss the cognitive and probabilistic-logical plausibility of this approach and demonstrate its applicability in computational persuasion.
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Notes
- 1.
Note that \(P(A)\) denotes the probability of argument A (the sum of probabilities of all possible worlds that accept A), while \(P(\{A\})\) denotes the probability of the possible world \(\{A\}\).
- 2.
We note that the study data contained examples of dialogues that resulted in a bigger belief change, however, we have chosen this one due to its interesting structure.
- 3.
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Potyka, N., Polberg, S., Hunter, A. (2019). Polynomial-Time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation. In: Kern-Isberner, G., Ognjanović, Z. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2019. Lecture Notes in Computer Science(), vol 11726. Springer, Cham. https://doi.org/10.1007/978-3-030-29765-7_7
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