Decision trees are simple, yet powerful, classification models used to classify categorical
and numerical data, and, despite their simplicity, they are commonly used in operations …

Land cover classification (LCC) refers to the task of classifying each pixel in satellite/aerial
imagery by predicting a label carrying information about its nature. Despite the importance of …

We introduce EHS+, a novel temporal-epistemic logic defined on temporal intervals
characterised by regular expressions. We investigate the complexity of verifying multi-agent …

Symbolic learning is the logic-based approach to machine learning. The mission of symbolic
learning is to provide algorithms and methodologies to extract logical information from data …

Real world applications as in industry and robotics need modelling rich and diverse
automated planning problems. Their resolution usually requires coordinated and concurrent …

Symbolic learning represents the most straightforward approach to interpretable modeling,
but its applications have been hampered by a single structural design choice: the adoption …

Real world applications of planning, like in industry and robotics, require modelling rich and
diverse scenarios. Their resolution usually requires coordinated and concurrent action …

The primary characteristic of interval temporal logic is that intervals, rather than points, are
taken as the primitive ontological entities. Given their generally bad computational behavior …

Interval temporal logics provide a natural framework for temporal reasoning about interval
structures over linearly ordered domains, where intervals are taken as first-class citizens …

The most representative interval temporal logic, called HS, was introduced by Halpern and
Shoham in the nineties. Recently, HS has been proposed as a suitable formalism for …