7 papers
Symbol Grounding in Neuro-Symbolic AI: A Gentle Introduction to Reasoning Shortcuts
Emanuele Marconato, Samuele Bortolotti, Emile van Krieken +6
Neuro-symbolic (NeSy) AI aims to develop deep neural networks whose predictions comply with prior knowledge encoding, e.g. safety or structural constraints. As such, it represents…
Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization
Matteo Pannacci, Andrea Fanti, Elena Umili +1
In this work we address the problem of training a Reinforcement Learning agent to follow multiple temporally-extended instructions expressed in Linear Temporal Logic in sub-symboli…
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.…