most citedLearning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approaches

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

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.AI2026★ 1 cited

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…