# Probabilistic ILP

**Check** Conformal prediction.

> Fonte: [Turning 30: New Ideas in Inductive Logic Programming](https://arxiv.org/abs/2002.11002)

## Introduction

- How pILP relates to:
  - ILP?
  - ASP?
  - RML?
- What 
  - tools?
  - methods?
  - theory?
    - Distributed semantics
  - applications?

### Overview of Bibliography and State of the Art

Recursion; Predicate Invention; Higher order, ASP Hypotheses; Optimality; Prolog, ASP, NNs 

## Context

### Kanren

### Inductive Logic Programming

### Answer Set Programming

### Relational Machine Learning

### SAT Solvers

## Tools

- [(mini)kanren](http://minikanren.org/)
  - in Julia: [MuKanren](https://github.com/latticetower/MuKanren.jl), [YA microkanren in Julia](https://www.philipzucker.com/yet-another-microkanren-in-julia/)!.
- [metagol | archive](https://github.com/metagol/metagol) _superseeded by **popper**._
- ILP: [popper](https://github.com/logic-and-learning-lab/Popper)
- ASP: [ILASP](https://github.com/ilaspltd/ILASP-releases)
- [Inspire | Kazmi et al. 2017]()
- ASP: [Potassco: clingo, clasp, ...](https://potassco.org/)
- [cplint (on SWISH)](http://cplint.ml.unife.it/)
  - exact probabilistic inference (PITA)
    - 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.
  - Monte Carlo inference (MCINTYRE) 
    - Fabrizio Riguzzi. MCINTYRE: A Monte Carlo system for probabilistic logic programming. Fundamenta Informaticae, 124(4):521-541, © IOS Press, 2013.
  - Metropolis/Hastings sampling
    - Arun Nampally and C. R. Ramakrishnan. Adaptive MCMC-Based Inference in Probabilistic Logic Programs. arXiv preprint arXiv:1403.6036, 2014.
  - parameter learning (EMBLEM)
    - 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.
  - SLIPCOVER algorithm for structure learning
    - 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.
  - LEMUR algorithm for structure learning
    - 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.

## Methods

## Theory

### Distributed Semantics

## Applications

### ELearning