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Comment by tannhaeuser | Hacker News Reader

FWIW, there's the entire field of Inductive Logic Programming focussed on learning/training (propositional or predicate logic) theories in Prolog syntax from examples presented as Prolog facts, with established packages such as Aleph and ProGolem also implemented in Prolog. See eg [1] for an ISO Prolog port and recent optimization/parallelization of Aleph.

[1]: https://quantumprolog.sgml.io/bioinformatics-demo/part1.html

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I finished my master's thesis in Inductive Logic Programming recently at Oxford. I'd say that the field has continued improving since Aleph, which was written in the late 90s.

Anyone interested could also take a look at Popper (https://github.com/logic-and-learning-lab/Popper) or this overview of the first 30 years of ILP (https://arxiv.org/abs/2008.07912)

It makes me glad to see that Aleph (and ILP more generally) are still being used. It's a really powerful and interpretable way of constructing code from examples.

I made a few contributions to Aleph as part of my PhD on doing transfer learning in ILP and really enjoyed working in Prolog.

https://mark.reid.name/bits/pubs/unsw07.pdf

Reading the first page of your thesis: "Central to this approach is a novel theory of task similarity that is defined in terms of syntactic properties of rules, called descriptions, which define what it means for rules to be similar". I wonder if you could use the current LLMs to obtain task similarity by semantic properties obtained by LLMs. (Just in case you develop the idea, put me as a coauthor for the main idea )).

Jokes apart, what makes me able to detect that kind of applications of LLM is that I have been looking how to combine LLM with rule based systems, using statistical methods, so I analyze anything that smells like that.

The likes of Popper for ILP are quite incredible in their own right although I would highlight to other readers that the article is about making reasoning explicit using Prolog as opposed to machine learning.

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