13 citations · 26 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
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…
Graph-based Event Log Repair
Sebastiano Dissegna, Chiara Di Francescomarino, Massimiliano Ronzani
The quality of event logs in Process Mining is crucial when applying any form of analysis to them. In real-world event logs, the acquisition of data can be non-trivial (e.g., due t…
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…
Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approaches
Stefano Branchi, Andrei Buliga, Chiara Di Francescomarino +4
Prescriptive Process Monitoring is an emerging area within Process Mining that focuses on recommending actions to optimize business outcomes. Most existing works prescribe pre-defi…
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…