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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2602.03486 |
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| _version_ | 1866914304129761280 |
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| author | Umili, Elena Argenziano, Francesco Capobianco, Roberto |
| author_facet | Umili, Elena Argenziano, Francesco Capobianco, Roberto |
| contents | Integrating logical knowledge into deep neural network training is still a hard challenge, especially for sequential or temporally extended domains involving subsymbolic observations. To address this problem, we propose DeepDFA, a neurosymbolic framework that integrates high-level temporal logic - expressed as Deterministic Finite Automata (DFA) or Moore Machines - into neural architectures. DeepDFA models temporal rules as continuous, differentiable layers, enabling symbolic knowledge injection into subsymbolic domains. We demonstrate how DeepDFA can be used in two key settings: (i) static image sequence classification, and (ii) policy learning in interactive non-Markovian environments. Across extensive experiments, DeepDFA outperforms traditional deep learning models (e.g., LSTMs, GRUs, Transformers) and novel neuro-symbolic systems, achieving state-of-the-art results in temporal knowledge integration. These results highlight the potential of DeepDFA to bridge subsymbolic learning and symbolic reasoning in sequential tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_03486 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DeepDFA: Injecting Temporal Logic in Deep Learning for Sequential Subsymbolic Applications Umili, Elena Argenziano, Francesco Capobianco, Roberto Machine Learning Artificial Intelligence Integrating logical knowledge into deep neural network training is still a hard challenge, especially for sequential or temporally extended domains involving subsymbolic observations. To address this problem, we propose DeepDFA, a neurosymbolic framework that integrates high-level temporal logic - expressed as Deterministic Finite Automata (DFA) or Moore Machines - into neural architectures. DeepDFA models temporal rules as continuous, differentiable layers, enabling symbolic knowledge injection into subsymbolic domains. We demonstrate how DeepDFA can be used in two key settings: (i) static image sequence classification, and (ii) policy learning in interactive non-Markovian environments. Across extensive experiments, DeepDFA outperforms traditional deep learning models (e.g., LSTMs, GRUs, Transformers) and novel neuro-symbolic systems, achieving state-of-the-art results in temporal knowledge integration. These results highlight the potential of DeepDFA to bridge subsymbolic learning and symbolic reasoning in sequential tasks. |
| title | DeepDFA: Injecting Temporal Logic in Deep Learning for Sequential Subsymbolic Applications |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.03486 |