deepproblog

DeepProbLog is an extension of ProbLog that integrates Probabilistic Logic Programming with deep learning by introducing the neural predicate.

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DeepProbLog is an extension of ProbLog that integrates Probabilistic Logic Programming with deep learning by introducing the neural predicate.

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Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

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Observed mentions: 2021-10-23 02:37:51 UTC to 2026-08-25 05:25:57 UTC.

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Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	1	1
2022	2022-01-01 00:00:00 UTC	2023-01-01 00:00:00 UTC	0	0
2023	2023-01-01 00:00:00 UTC	2024-01-01 00:00:00 UTC	1	1
2024	2024-01-01 00:00:00 UTC	2025-01-01 00:00:00 UTC	0	0
2025	2025-01-01 00:00:00 UTC	2026-01-01 00:00:00 UTC	1	1
2026 (partial)	2026-01-01 00:00:00 UTC	2027-01-01 00:00:00 UTC	1	1

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That assumes it can even be done. It's worth looking into. There have been some projects in those areas. Mixing probabilistic logic with deep learning: https://arxiv.org/abs/1808.08485 https://github.com/ML-KULeuven/deepproblog Combining decision trees with neural nets for interpretability: https://arxiv.org/abs/2011.07553 https://arxiv.org/pdf/2106.02824v1 https://arxiv.org/pdf/1806.06988 https://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-... It looks like model transfer from uninterpretable, pretrained models to interpretable models is the best strategy to keep using. That also justifies work like Ai2's OLMo model where all pretraining data is available to use other techniques, like those in search engines, to help explainable models connect facts back to source material.

There's a nice book[1][2] about Prolog, with modern characteristics. Moreover, there are things like ProbLog[3] and DeepProbLog[4] that allow you to use probabilistic reasoning and power of machine learning. I am personally looking forward for Scryer Prolog[5] to achieve its goals. [1] https://www.metalevel.at/prolog [2] https://github.com/triska/the-power-of-prolog [3] https://github.com/ML-KULeuven/problog [4] https://github.com/ML-KULeuven/deepproblog [5] https://github.com/mthom/scryer-prolog

There is also ProbLog (Probabilistic Prolog)[1][2] and even extension of it with a deep learning - DeepProbLog[3]. Personally, though, I hope that new ISO Prolog implementation in Rust, aiming for the performance, Scryer Prolog[4] will add the probabilistic capability [5]. [1] https://dtai.cs.kuleuven.be/problog/ [2] https://github.com/ML-KULeuven/problog [3] https://github.com/ML-KULeuven/deepproblog [4] http://github.com/mthom/scryer-prolog/ [5] https://github.com/mthom/scryer-prolog/issues/69