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Research Note on Uncertain Probabilities and Abstract Argumentation
The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1. 5C (medium confidence)."
Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement
Causal models are playing an increasingly important role in machine learning, particularly in the realm of explainable AI.
Argumentative XAI: A Survey
Explainable AI (XAI) has been investigated for decades and, together with AI itself, has witnessed unprecedented growth in recent years.
Influence-Driven Explanations for Bayesian Network Classifiers
One of the most pressing issues in AI in recent years has been the need to address the lack of explainability of many of its models.
Automata for Infinite Argumentation Structures
The theory of abstract argumentation frameworks (afs) has, in the main, focused on finite structures, though there are many significant contexts where argumentation can be regarded as a process involving infinite objects.
AFRA: Argumentation framework with recursive attacks
The issue of representing attacks to attacks in argumentation is receiving an increasing attention as a useful conceptual modelling tool in several contexts.
A Labelling Framework for Probabilistic Argumentation
The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities.
