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Inductive Logic Programming is really interesting. I'd really like to explore similarities between ILP and the synthesis used in Barliman--I suspect Barliman's

Inductive Logic Programming is really interesting. I'd really like to explore similarities between ILP and the synthesis used in Barliman--I suspect Barliman's example-based synthesis could get a real boost from ILP. One book I highly recommend, and which I've found very accessible, is Ivan Bratko's 'Prolog Programming for Artificial Intelligence'. The 4th edition has a section on ILP. If you are interested in ILP and also in Barliman, maybe this is a topic we could explore together.

YeGoblynQueenne 10y ago
A good resource for relational learning in general including ILP is this: http://www.springer.com/gp/book/9783540200406 http://www.springer.com/gp/book/9783540200406 And this is a good introduction to the statistical side of things, a.k.a. statistical relational learning: http://www.cs.umd.edu/srl-book/ http://www.cs.umd.edu/srl-book/ Also, if you're considering adding stochastic search this might be a good pointer: https://dtai.cs.kuleuven.be/problog/ https://dtai.cs.kuleuven.be/problog/ Problog is a probabilistic Prolog. Above is the implementation in Python but there's a few Prolog versions floating around, unfortunately the ones I tried did not seem to work out of the box.
- will_byrd 10y ago
  Thanks for the great links! I really enjoyed the Statistical Relational Learning book. Rob Zinkov and I have worked on two prototypes of probKanren (https://github.com/webyrd/probKanren https://github.com/webyrd/probKanren), a probabilistic version of miniKanren inspired partly by the Hakaru language (http://indiana.edu/~ppaml/HakaruTutorial.html http://indiana.edu/~ppaml/HakaruTutorial.html, https://github.com/hakaru-dev/hakaru https://github.com/hakaru-dev/hakaru). We learned a lot from our two prototypes of probKanren, but neither version is ready for real use (and neither version is documented!). Rob and I have taken a step back, and are now working with Evan Donahue on just adding stochastic search to miniKanren. If you are interested in joining us, please let me know! :)
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YeGoblynQueenne 10y ago

A good resource for relational learning in general including ILP is this: http://www.springer.com/gp/book/9783540200406 http://www.springer.com/gp/book/9783540200406 And this is a good introduction to the statistical side of things, a.k.a. statistical relational learning: http://www.cs.umd.edu/srl-book/ http://www.cs.umd.edu/srl-book/ Also, if you're considering adding stochastic search this might be a good pointer: https://dtai.cs.kuleuven.be/problog/ https://dtai.cs.kuleuven.be/problog/ Problog is a probabilistic Prolog. Above is the implementation in Python but there's a few Prolog versions floating around, unfortunately the ones I tried did not seem to work out of the box.

will_byrd 10y ago
Thanks for the great links! I really enjoyed the Statistical Relational Learning book. Rob Zinkov and I have worked on two prototypes of probKanren (https://github.com/webyrd/probKanren https://github.com/webyrd/probKanren), a probabilistic version of miniKanren inspired partly by the Hakaru language (http://indiana.edu/~ppaml/HakaruTutorial.html http://indiana.edu/~ppaml/HakaruTutorial.html, https://github.com/hakaru-dev/hakaru https://github.com/hakaru-dev/hakaru). We learned a lot from our two prototypes of probKanren, but neither version is ready for real use (and neither version is documented!). Rob and I have taken a step back, and are now working with Evan Donahue on just adding stochastic search to miniKanren. If you are interested in joining us, please let me know! :)
- deleted 10y ago
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