Polynomial-time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation (Technical Report)
This work addresses computational challenges for applications like persuasion dialogues, though it appears incremental as it builds on existing update operators with a focus on efficiency.
The paper tackles the computational bottleneck in probabilistic epistemic argumentation, where updates depend exponentially on the number of arguments, by relating probability function updates to compact representations that enable polynomial-time updates, demonstrating applicability in computational persuasion.
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.