# Polynomial-time Updates of Epistemic States in a Fragment of Probabilistic Epistemic Argumentation (Technical Report)

## TL;DR

This paper introduces a method for efficiently updating probabilistic epistemic states in argumentation frameworks, enabling polynomial-time computations that were previously exponential, thus improving practical reasoning in persuasion scenarios.

## Contribution

It presents a novel approach linking probability function updates to compact representations, allowing polynomial-time updates in probabilistic epistemic argumentation.

## Key findings

- Polynomial-time update algorithms for epistemic states.
- Applicability demonstrated in computational persuasion.
- Enhanced computational efficiency over traditional methods.

## 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.