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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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