Aleph

The Aleph Manual. This document provides reference information on A Learning Engine for Proposing Hypotheses (Aleph). Aleph is an Inductive Logic Programming (ILP) system. This manual is not intended to be a tutorial on ILP. A good introduction to the theory, implementation and applications of ILP can be found in S.H. Muggleton and L. De Raedt (1994), Inductive Logic Programming: Theory and Methods, Jnl. Logic Programming, 19,20:629--679, available at ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/lpj.ps.gz. Aleph is intended to be a prototype for exploring ideas. Earlier incarnations (under the name P-Progol) originated in 1993 as part of a fun project undertaken by Ashwin Srinivasan and Rui Camacho at Oxford University. The main purpose was to understand ideas of inverse entailment which eventually appeared in Stephen Muggleton’s 1995 paper: Inverse Entailment and Progol, New Gen. Comput., 13:245-286, available at ftp://ftp.cs.york.ac.uk/pub/ML_GROUP/Papers/InvEnt.ps.gz. Since then, the implementation has evolved to emulate some of the functionality of several other ILP systems. Some of these of relevance to Aleph are: CProgol, FOIL, FORS, Indlog, MIDOS, SRT, Tilde, and WARMR. See section Related versions and programs for more details on obtaining some of these programs.


References in zbMATH (referenced in 50 articles )

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  1. Furelos-Blanco, Daniel; Law, Mark; Jonsson, Anders; Broda, Krysia; Russo, Alessandra: Induction and exploitation of subgoal automata for reinforcement learning (2021)
  2. Bekker, Jessa; Davis, Jesse: Learning from positive and unlabeled data: a survey (2020)
  3. Cropper, Andrew; Evans, Richard; Law, Mark: Inductive general game playing (2020)
  4. Cropper, Andrew; Morel, Rolf; Muggleton, Stephen: Learning higher-order logic programs (2020)
  5. Gromowski, Mark; Siebers, Michael; Schmid, Ute: A process framework for inducing and explaining Datalog theories (2020)
  6. Lavrač, Nada; Škrlj, Blaž; Robnik-Šikonja, Marko: Propositionalization and embeddings: two sides of the same coin (2020)
  7. Shakerin, Farhad; Gupta, Gopal: White-box induction from SVM models: explainable AI with logic programming (2020)
  8. Srinivasan, Ashwin; Vig, Lovekesh; Shroff, Gautam: Constructing generative logical models for optimisation problems using domain knowledge (2020)
  9. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  10. Michelioudakis, Evangelos; Artikis, Alexander; Paliouras, Georgios: Semi-supervised online structure learning for composite event recognition (2019)
  11. Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
  12. Srinivasan, Ashwin; Vig, Lovekesh; Bain, Michael: Logical explanations for deep relational machines using relevance information (2019)
  13. Wielemaker, Jan; Riguzzi, Fabrizio; Kowalski, Robert A.; Lager, Torbjörn; Sadri, Fariba; Calejo, Miguel: Using SWISH to realize interactive web-based tutorials for logic-based languages (2019)
  14. Dutta, Haimonti; Srinivasan, Ashwin: Consensus-based modeling using distributed feature construction with ILP (2018)
  15. Law, Mark; Russo, Alessandra; Broda, Krysia: The complexity and generality of learning answer set programs (2018)
  16. Muggleton, Stephen H.; Schmid, Ute; Zeller, Christina; Tamaddoni-Nezhad, Alireza; Besold, Tarek: Ultra-strong machine learning: comprehensibility of programs learned with ILP (2018)
  17. Paes, Aline; Zaverucha, Gerson; Santos Costa, Vítor: On the use of stochastic local search techniques to revise first-order logic theories from examples (2017)
  18. Srinivasan, Ashwin; Bain, Michael: An empirical study of on-line models for relational data streams (2017)
  19. Adhikari, Prem Raj; Vavpetič, Anže; Kralj, Jan; Lavrač, Nada; Hollmén, Jaakko: Explaining mixture models through semantic pattern mining and banded matrix visualization (2016)
  20. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)

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