Neurosymbolic Integration of Linear Temporal Logic in Non Symbolic Domains

Abstract

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.