Random forests are decision tree ensembles that can be used to solve a variety of machine
learning problems. However, as the number of trees and their individual size can be large …

This paper presents a new deep learning approach to sceptical and credulous acceptance
of arguments, two key problems in Abstract Argumentation that are most commonly solved …

An Efficient Algorithm for Skeptical Preferred Acceptance in Dynamic Argumentation
Frameworks Page 1 Introduction Incremental Computation Experiments Conclusions and future …

We examine the impact of both training and test data selection in machine learning
applications for abstract argumentation, in terms of prediction accuracy and generalizability …

We address the task of selecting the fastest algorithm, in terms of runtime, for determining
skeptical acceptance under preferred semantics in abstract argumentation frameworks out of …

Motivated by the fact that argumentation is intrinsically a dynamic process, the study of
representational and computational aspects of dynamics in argumentation is starting to gain …

Abstract argumentation and Dung's framework are popular for modeling and evaluating
arguments in artificial intelligence. We consider various counting problems in abstract …

Abstract Argumentation is a widely applied framework for modeling and evaluating
arguments and its reasoning with various applications. Popular frameworks are abstract …

Abstract Argumentation is a well-established formalism for nonmonotonic reasoning and a
vibrant area of research in AI. Claim-augmented argumentation frameworks (CAFs) have …

We explain how abstract argumentation problems can be encoded as Markov networks.
From a computational perspective, this allows reducing argumentation tasks like finding …