- Published 2015

Argumentation can be modelled at an abstract level using an argument graph (i.e. a directed graph where each node denotes an argument and each arc denotes an attack by one argument on another). Since argumentation involves uncertainty, it is potentially valuable to consider how this can quantified in argument graphs. In this talk, we will consider two probabilistic approaches for modeling uncertainty in argumentation. The first is the structural approach which involves a probability distribution over the subgraphs of the argument graph, and this can be used to represent the uncertainty over the structure of the graph. The second is the epistemic approach which involves a probability distribution over the subsets of the arguments, and this can be used to represent the uncertainty over which arguments are believed. The epistemic approach can be constrained to be consistent with Dungs dialectical semantics, but it can also be used as a potential valuable alternative to Dungs dialectical semantics. We will consider applications of probabilistic argumentation in handling enthymemes (arguments with incomplete premises) and in selecting moves in an argumentation dialogue. IJCAI-15 Workshop on Weighted Logics for Artiticial Intelligence (WL4AI-2015)

Showing 1-10 of 136 references

Highly Influential

6 Excerpts

Highly Influential

5 Excerpts

Highly Influential

7 Excerpts

Highly Influential

6 Excerpts

Highly Influential

4 Excerpts

Highly Influential

8 Excerpts

Highly Influential

9 Excerpts

Highly Influential

3 Excerpts

Highly Influential

2 Excerpts

Highly Influential

6 Excerpts

@inproceedings{Finger2015WorkingPO,
title={Working Papers of the IJCAI - 2015 Workshop on Weighted Logics for Artificial Intelligence WL 4 AI - 2015 July 27 , 2015 Buenos Aires ( Argentina )},
author={Marcelo Finger and Lluis Godo and Henri Prade},
year={2015}
}