i've come to appreciate, over the past 2 years of heavy Prolog use, that all coding should be (eventually) be done in Prolog.
It's one of few languages that is simultaneously a standalone logical formalism, and a standalone representation of computation. (With caveats and exceptions, I know). So a Prolog program can stand in as a document of all facts, rules and relations that a person/organization understands/declares to be true. Even if AI writes code for us, we should expect to have it presented and manipulated as a logical formalism.
Now if someone cares to argue that some other language/compiler is better at generating more performant code on certain architectures, then that person can declare their arguments in a logical formalism (Prolog) and we can use Prolog to translate between language representations, compile, optimize, etc.
The generated programs are only technically Prolog programs. They use CLPFD, which makes these constraint programs. Prolog programs are quite a bit more tricky with termination issues. I wouldn’t have nitpicked if it wasn’t in the title.
Also, the experiment method has some flaws. Problems are hand-picked out of a random subset of the full set. Why not run the full set?
This is basically the LLM modulo approach recommended by Prof. Subbarao Kambhampati. Interesting but only works mostly for problems that have some math/first degree logic puzzle at their heart. Will fail at improving perf at ARC-AGI for example...
Difficult to mimic reasoning by basic trial and error then hoping for the best:
https://www.lycee.ai/blog/why-sam-altman-is-wrong
I tried an experiment with this using a Prolog interpreter with GPT-4 to try to answer complex logic questions. I found that it was really difficult because the model didn't seem to know Prolog well enough to write a description of any complexity.
It seems like you used an interpreter in the loop which is likely to help. I'd also be interested to see how o1 would do in a task like this or if it even makes sense to use something like prolog if the models can backtrack during the "thinking" phase
z5h ·6 hours ago
It's one of few languages that is simultaneously a standalone logical formalism, and a standalone representation of computation. (With caveats and exceptions, I know). So a Prolog program can stand in as a document of all facts, rules and relations that a person/organization understands/declares to be true. Even if AI writes code for us, we should expect to have it presented and manipulated as a logical formalism.
Now if someone cares to argue that some other language/compiler is better at generating more performant code on certain architectures, then that person can declare their arguments in a logical formalism (Prolog) and we can use Prolog to translate between language representations, compile, optimize, etc.
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gorkempacaci ·4 hours ago
Also, the experiment method has some flaws. Problems are hand-picked out of a random subset of the full set. Why not run the full set?
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fsndz ·5 hours ago
pjmlp ·7 hours ago
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a1j9o94 ·7 hours ago
It seems like you used an interpreter in the loop which is likely to help. I'd also be interested to see how o1 would do in a task like this or if it even makes sense to use something like prolog if the models can backtrack during the "thinking" phase
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