Francisco Coelho / zugzwang

README.md 2.61 KB

Probabilistic ILP

Fonte: Turning 30: New Ideas in Inductive Logic Programming

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
  • metagol | archive superseeded by popper.
  • ILP: popper
  • ASP: ILASP
  • Inspire | Kazmi et al. 2017
  • ASP: Potassco: clingo, clasp, ...
  • cplint (on SWISH)
    • 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