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Any Machine Learning engineers here willing to take the challenge of generating a prolog engine from the natural language descriptions of constraints?
by sansnomme 7y ago
Any Machine Learning engineers here willing to take the challenge of generating a prolog engine from the natural language descriptions of constraints?
- Mathnerd314 7y agoIt seems tricky since ML models aren't really good at model building and code needs consistent variable names and structuring. The OpenGPT model is OK at doing sentences but can't really keep track of player state. The RNN in https://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://karpathy.github.io/2015/05/21/rnn-effectiveness/ can't use variable names consistently. The recent papers in https://github.com/src-d/awesome-machine-learning-on-source-code#program-synthesis-and-induction https://github.com/src-d/awesome-machine-learning-on-source-... still can't use names consistently. Those kinds of issues don't prevent someone from whipping up something, but guarantee it'll have terrible accuracy. Anybody trying to use it would probably spend more time debugging the generated Prolog than they would just writing the Prolog themselves. PL research isn't really focused on translation anyway; the current trends I've seen are block-structured editors where you would snap things together to form a constraint, probably using a formal mathematics-inspired syntax similar to the LaTeX stuff that this catalog is using, and using SMT solvers to avoid having to deal with the complexities of procedural languages.