activity
20222026
most citedLearning to act: a Reinforcement Learning approach to recommend the best next activities

13 citations · 31 across the 6 of their papers we have counts for

collaborators

7 papers

math.FA2026

Expansive operators on Fréchet spaces without continuous norm

Antoni López-Martínez, Quentin Menet

We investigate the existence of (metrically) expansive operators on Fréchet spaces without continuous norm. We first solve a problem posed by Bernardes, Caraballo, Darji, Fávaro, a…

cs.AI2026

Time to Reason: Scalable Neurosymbolic Learning for LTLf via Fuzzy Semantics

Riccardo Andreoni, Andrei Buliga, Alessandro Daniele +4

Neurosymbolic (NeSy) Artificial Intelligence aims to integrate Deep Learning (DL) architectures with symbolic reasoning. While initial NeSy approaches have targeted mainly symbolic…

cs.AI2025

T-ILR: a Neurosymbolic Integration for LTLf

Riccardo Andreoni, Andrei Buliga, Alessandro Daniele +3

State-of-the-art approaches for integrating symbolic knowledge with deep learning architectures have demonstrated promising results in static domains. However, methods to handle te…

cs.AI2025★ 6 cited

Generating Counterfactual Explanations Under Temporal Constraints

Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini +2

Counterfactual explanations are one of the prominent eXplainable Artificial Intelligence (XAI) techniques, and suggest changes to input data that could alter predictions, leading t…

cs.DB2024★ 6 cited

Generating the Traces You Need: A Conditional Generative Model for Process Mining Data

Riccardo Graziosi, Massimiliano Ronzani, Andrei Buliga +5

In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing th…

cs.AI2024★ 6 cited

Guiding the generation of counterfactual explanations through temporal background knowledge for Predictive Process Monitoring

Andrei Buliga, Chiara Di Francescomarino, Chiara Ghidini +2

Counterfactual explanations suggest what should be different in the input instance to change the outcome of an AI system. When dealing with counterfactual explanations in the field…

cs.AI2022★ 13 cited

Learning to act: a Reinforcement Learning approach to recommend the best next activities

Stefano Branchi, Chiara Di Francescomarino, Chiara Ghidini +3

The rise of process data availability has recently led to the development of data-driven learning approaches. However, most of these approaches restrict the use of the learned mode…