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README.md
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| 1 | +# Probabilistic ILP | ||
| 2 | + | ||
| 3 | +> Fonte: [Turning 30: New Ideas in Inductive Logic Programming](https://arxiv.org/abs/2002.11002) | ||
| 4 | + | ||
| 5 | +## Introduction | ||
| 6 | + | ||
| 7 | +- How pILP relates to: | ||
| 8 | + - ILP? | ||
| 9 | + - ASP? | ||
| 10 | + - RML? | ||
| 11 | +- What | ||
| 12 | + - tools? | ||
| 13 | + - methods? | ||
| 14 | + - theory? | ||
| 15 | + - Distributed semantics | ||
| 16 | + - applications? | ||
| 17 | + | ||
| 18 | +### Overview of Bibliography and State of the Art | ||
| 19 | + | ||
| 20 | +Recursion; Predicate Invention; Higher order, ASP Hypotheses; Optimality; Prolog, ASP, NNs | ||
| 21 | + | ||
| 22 | +## Context | ||
| 23 | + | ||
| 24 | +### Kanren | ||
| 25 | + | ||
| 26 | +### Inductive Logic Programming | ||
| 27 | + | ||
| 28 | +### Answer Set Programming | ||
| 29 | + | ||
| 30 | +### Relational Machine Learning | ||
| 31 | + | ||
| 32 | +### SAT Solvers | ||
| 33 | + | ||
| 34 | +## Tools | ||
| 35 | + | ||
| 36 | +- [(mini)kanren](http://minikanren.org/) | ||
| 37 | + - in Julia: [MuKanren](https://github.com/latticetower/MuKanren.jl), [YA microkanren in Julia](https://www.philipzucker.com/yet-another-microkanren-in-julia/)!. | ||
| 38 | +- [metagol | archive](https://github.com/metagol/metagol) _superseeded by **popper**._ | ||
| 39 | +- ILP: [popper](https://github.com/logic-and-learning-lab/Popper) | ||
| 40 | +- ASP: [ILASP](https://github.com/ilaspltd/ILASP-releases) | ||
| 41 | +- [Inspire | Kazmi et al. 2017]() | ||
| 42 | +- ASP: [Potassco: clingo, clasp, ...](https://potassco.org/) | ||
| 43 | +- [cplint (on SWISH)](http://cplint.ml.unife.it/) | ||
| 44 | + - exact probabilistic inference (PITA) | ||
| 45 | + - Fabrizio Riguzzi and Terrance Swift. Well-definedness and efficient inference for probabilistic logic programming under the distribution semantics. Theory and Practice of Logic Programming, 13(Special Issue 02 - 25th Annual GULP Conference):279-302, © Cambridge University Press, March 2013. | ||
| 46 | + - Monte Carlo inference (MCINTYRE) | ||
| 47 | + - Fabrizio Riguzzi. MCINTYRE: A Monte Carlo system for probabilistic logic programming. Fundamenta Informaticae, 124(4):521-541, © IOS Press, 2013. | ||
| 48 | + - Metropolis/Hastings sampling | ||
| 49 | + - Arun Nampally and C. R. Ramakrishnan. Adaptive MCMC-Based Inference in Probabilistic Logic Programs. arXiv preprint arXiv:1403.6036, 2014. | ||
| 50 | + - parameter learning (EMBLEM) | ||
| 51 | + - Elena Bellodi and Fabrizio Riguzzi. Expectation Maximization over binary decision diagrams for probabilistic logic programs. Intelligent Data Analysis, 17(2):343-363, © IOS Press, 2013. | ||
| 52 | + - SLIPCOVER algorithm for structure learning | ||
| 53 | + - Elena Bellodi and Fabrizio Riguzzi. Structure learning of probabilistic logic programs by searching the clause space. Theory and Practice of Logic Programming, 15(2):169-212, © Cambridge University Press, 2015. | ||
| 54 | + - LEMUR algorithm for structure learning | ||
| 55 | + - Nicola Di Mauro, Elena Bellodi, and Fabrizio Riguzzi. Bandit-based Monte-Carlo structure learning of probabilistic logic programs. Machine Learning, 100(1):127-156, © Springer International Publishing, July 2015. | ||
| 56 | + | ||
| 57 | +## Methods | ||
| 58 | + | ||
| 59 | +## Theory | ||
| 60 | + | ||
| 61 | +### Distributed Semantics | ||
| 62 | + | ||
| 63 | +## Applications | ||
| 64 | + | ||
| 65 | +### ELearning | ||
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