5 papers
DeepDFA: Injecting Temporal Logic in Deep Learning for Sequential Subsymbolic Applications
Elena Umili, Francesco Argenziano, Roberto Capobianco
Integrating logical knowledge into deep neural network training is still a hard challenge, especially for sequential or temporally extended domains involving subsymbolic observatio…
Fully Learnable Neural Reward Machines
Hazem Dewidar, Elena Umili
Non-Markovian Reinforcement Learning (RL) tasks present significant challenges, as agents must reason over entire trajectories of state-action pairs to make optimal decisions. A co…
Defining and Monitoring Complex Robot Activities via LLMs and Symbolic Reasoning
Francesco Argenziano, Elena Umili, Francesco Leotta +1
Recent years have witnessed a growing interest in automating labor-intensive and complex activities, i.e., those consisting of multiple atomic tasks, by deploying robots in dynamic…
Neural Reward Machines
Elena Umili, Francesco Argenziano, Roberto Capobianco
Non-markovian Reinforcement Learning (RL) tasks are very hard to solve, because agents must consider the entire history of state-action pairs to act rationally in the environment.…
DeepDFA: Automata Learning through Neural Probabilistic Relaxations
Elena Umili, Roberto Capobianco
In this work, we introduce DeepDFA, a novel approach to identifying Deterministic Finite Automata (DFAs) from traces, harnessing a differentiable yet discrete model. Inspired by bo…