Dipartimento di Ingegneria informatica, automatica e gestionale

Linear Temporal Logic (LTL) is widely used to specify temporal relationships and dynamic constraints for autonomous agents. However, in order to be used in practice in real-world domains, this high-level knowledge must be grounded in the task domain and integrated with perception and learning modules that are intrinsically continuous and subsymbolic. In this short paper, I describe many ways to integrate formal symbolic knowledge in LTL in non-symbolic domains using deep-learning modules and neuro-symbolic techniques, and I discuss the results obtained in different kinds of applications, ranging from classification of complex data to DFA induction to non-Markovian Reinforcement Learning.