Causal inference is a powerful modeling tool for explanatory analysis, which might enable
current machine learning to become explainable. How to marry causal inference with …

Abstract Information technology is so ubiquitous and AI's progress so inspiring that also
legal professionals experience its benefits and have high expectations. At the same time, the …

A significant challenge for recommender systems (RSs), and in fact for AI systems in
general, is the systematic definition of explanations for outputs in such a way that both the …

Argumentation mining is an application of natural language processing (NLP) that emerged
a few years ago and has recently enjoyed considerable popularity, as demonstrated by a …

Argument schemes are abstractions substantiating the inferential connection between
premise (s) and conclusion in argumentative communication. Identifying such conventional …

BDI agents act in response to external inputs and their internal plan library. Understanding
the root cause of BDI agent action is often difficult, and in this paper we present a dialogue …

Model Multiplicity (MM), also known as predictive multiplicity or the Rashomon Effect, refers
to a scenario where multiple, equally performing machine learning (ML) models may be …

Abstract argumentation constitutes both a major research strand and a key approach that
provides the core reasoning engine for a multitude of formalisms in computational …

Consider an argument A that is attacked by an argument B, while A is preferred to B. Existing
approaches will either ignore the attack or reverse it. In this paper we introduce a new …

This paper is a contribution to the research on dynamics in assumption-based
argumentation (ABA). We investigate situations where a given knowledge base undergoes …