Markov Chain Monte Carlo for program synthesis isn't exactly novel. The most immediate reference I thought of is Josh Tenenbaum's [1]. There's also a lot of de

Markov Chain Monte Carlo for program synthesis isn't exactly novel. The most immediate reference I thought of is Josh Tenenbaum's [1]. There's also a lot of demos in WebPPL (web probabilistic programming language)[2] like [3] for the synthesis of 3D space-ships. I highly recommend their associated books on The Design and Implementation of Probabilistic Programming Languages [4] and Probabilistic Models of Cognition [5]. I also highly recommend taking a look at the publications of the MIT Probabilistic Computing Project [6]. [1] Human-level concept learning through probabilistic program induction. https://www.cs.cmu.edu/~rsalakhu/papers/LakeEtAl2015Science.pdf https://www.cs.cmu.edu/~rsalakhu/papers/LakeEtAl2015Science.... [2] http://webppl.org/ http://webppl.org/ [3] https://dritchie.github.io/web-procmod/ https://dritchie.github.io/web-procmod/ [4] https://dippl.org/ https://dippl.org/ [5] http://probmods.org/ http://probmods.org/ [6] http://probcomp.csail.mit.edu/ http://probcomp.csail.mit.edu/