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- W4386473786 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." @default.
- W4386473786 created "2023-09-07" @default.
- W4386473786 creator A5072591423 @default.
- W4386473786 date "2023-01-01" @default.
- W4386473786 modified "2023-09-27" @default.
- W4386473786 title "Neurosymbolic Integration of Linear Temporal Logic in Non Symbolic Domains" @default.
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- W4386473786 doi "https://doi.org/10.1007/978-3-031-43264-4_41" @default.
- W4386473786 hasPublicationYear "2023" @default.
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