3 citations · 4 across the 7 of their papers we have counts for
6 papers · 1 filter
Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach
Daniele Fossemò, Filippo Mignosi, Giuseppe Placidi +3
In this paper, we propose using Learning from Answer Sets to approximate black-box models, such as Neural Networks (NN), in the specific case of learning user preferences. We speci…
A Translation of Probabilistic Event Calculus into Markov Decision Processes
Lyris Xu, Fabio Aurelio D'Asaro, Luke Dickens
Probabilistic Event Calculus (PEC) is a logical framework for reasoning about actions and their effects in uncertain environments, which enables the representation of probabilistic…
Weighted Assumption Based Argumentation to reason about ethical principles and actions
Paolo Baldi, Fabio Aurelio D'Asaro, Abeer Dyoub +1
We augment Assumption Based Argumentation (ABA for short) with weighted argumentation. In a nutshell, we assign weights to arguments and then derive the weight of attacks between A…
A Unifying Framework for Learning Argumentation Semantics
Zlatina Mileva, Antonis Bikakis, Fabio Aurelio D'Asaro +2
Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artif…
An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems
Fabio Aurelio D'Asaro, Luca Raggioli, Salim Malek +2
We present and discuss a runtime architecture that integrates sensorial data and classifiers with a logic-based decision-making system in the context of an e-Health system for the…
Foundations for a Probabilistic Event Calculus
Fabio Aurelio D'Asaro, Antonis Bikakis, Luke Dickens +1
We present PEC, an Event Calculus (EC) style action language for reasoning about probabilistic causal and narrative information. It has an action language style syntax similar to t…