6 papers
An Unofficial FastLAS Tutorial: A Programmer's Guide
Fabio Aurelio D'Asaro
FastLAS is a scalable system for Inductive Logic Programming (ILP): you give it some background knowledge, a language bias, and a set of examples, and it searches for a set of logi…
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…
Checking Trustworthiness of Probabilistic Computations in a Typed Natural Deduction System
Fabio Aurelio D'Asaro, Francesco Genco, Giuseppe Primiero
In this paper we present the probabilistic typed natural deduction calculus TPTND, designed to reason about and derive trustworthiness properties of probabilistic computational pro…