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Neural Guided Constraint Logic Programming for Program Synthesis

Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya

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Renjie Liao, William E. Byrd, Matthew Might, Raquel Urtasun, Richard Zemel

2018 · arXiv

Programming languagesMachine learningPsychology
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From the abstract

Synthesizing programs using example input/outputs is a classic problem in artificial intelligence. We present a method for solving Programming By Example (PBE) problems by using a neural model to guide the search of a constraint logic programming system called miniKanren. Crucially, the neural model uses miniKanren's internal representation as input; miniKanren represents a PBE problem as recursive constraints imposed by the provided examples. We explore Recurrent Neural Network and Graph Neural Network models. We contribute a modified miniKanren, drivable by an external agent, available at https://github.com/xuexue/neuralkanren. We show that our neural-guided approach using constraints can synthesize programs faster in many cases, and importantly, can generalize to larger problems.

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3 selected excerpts · 3 total mentions

eihli
Neutral mention · rule
Hey! Thanks for all of the work you do. I love reading your stuff. Lately, I've been playing a lot with the code from Neural Guided Constraint Logic Programming for Program Synthesis [1]. I want to apply it to searching for neural network architectures. Something like: (define-relation (layero layer in out) ;; [2] (conde ((== layer `(Linear ,in ,out))) ((== layer `(Relu ,in ,out)) (== in out)) ((fresh (percent) (== layer `(Dropout ,in ,out ,percent)) (== in out) (countero percent) (<o '(1) percent) (<o percent '(0 0 1 0 0 1 1)))))) And a `layerso` that is just a sequence of `layers`. And then use some metric that captures how effective the training run was and use that…
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metric for teaching the search network. I'm sure there's better ways to do it than the way I'm going about it. But it's a fun way to learn a bunch of cool new stuff. Thanks for getting me started down this path! [1] https://arxiv.org/abs/1809.02840 [2] https://github.com/eihli/reason/blob/523920773b7040325c8098a...
Original comment What Is miniKanren?
abeppu
Neutral mention · rule
> I call it program synthesis. In ChatGPT you can get ChatGPT to generate code, so this kind of already exists. This is cool, but to be clear work on program synthesis predates ChatGPT by _decades_. For a long time the work was searching for a program which met some behavioral specification. Emphasis shifted later towards inductive "programming-by-example" where we synthesize a program based on a small set of inputs and outputs, in part because providing those examples is often easier than thinking through an exact "specification" of what you want. Some work from the '70s https://dl.acm.org/doi/10.1145/362566.362568 https://dl.acm.org/doi/10.5555/1624626.1624666 Some 21st century work on inductive synthesis https://link.springer.com/chapter/10.1007/978-3-319-21690-4_... ML + symbolic inductive synthesis: https://arxiv.org/pdf/1809.02840v1.pdf
Original comment Compilers for the Future

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