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 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 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 47 articles )

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  1. Bekker, Jessa; Davis, Jesse: Learning from positive and unlabeled data: a survey (2020)
  2. Cropper, Andrew; Evans, Richard; Law, Mark: Inductive general game playing (2020)
  3. Cropper, Andrew; Morel, Rolf; Muggleton, Stephen: Learning higher-order logic programs (2020)
  4. Lavrač, Nada; Škrlj, Blaž; Robnik-Šikonja, Marko: Propositionalization and embeddings: two sides of the same coin (2020)
  5. Srinivasan, Ashwin; Vig, Lovekesh; Shroff, Gautam: Constructing generative logical models for optimisation problems using domain knowledge (2020)
  6. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  7. Michelioudakis, Evangelos; Artikis, Alexander; Paliouras, Georgios: Semi-supervised online structure learning for composite event recognition (2019)
  8. Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
  9. Srinivasan, Ashwin; Vig, Lovekesh; Bain, Michael: Logical explanations for deep relational machines using relevance information (2019)
  10. 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)
  11. Dutta, Haimonti; Srinivasan, Ashwin: Consensus-based modeling using distributed feature construction with ILP (2018)
  12. Law, Mark; Russo, Alessandra; Broda, Krysia: The complexity and generality of learning answer set programs (2018)
  13. Muggleton, Stephen H.; Schmid, Ute; Zeller, Christina; Tamaddoni-Nezhad, Alireza; Besold, Tarek: Ultra-strong machine learning: comprehensibility of programs learned with ILP (2018)
  14. Paes, Aline; Zaverucha, Gerson; Costa, Vítor Santos: On the use of stochastic local search techniques to revise first-order logic theories from examples (2017)
  15. Srinivasan, Ashwin; Bain, Michael: An empirical study of on-line models for relational data streams (2017)
  16. Adhikari, Prem Raj; Vavpetič, Anže; Kralj, Jan; Lavrač, Nada; Hollmén, Jaakko: Explaining mixture models through semantic pattern mining and banded matrix visualization (2016)
  17. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  18. Kaalia, Rama; Srinivasan, Ashwin; Kumar, Amit; Ghosh, Indira: ILP-assisted de novo drug design (2016) ioport
  19. Law, Mark; Russo, Alessandra; Broda, Krysia: Iterative learning of answer set programs from context dependent examples (2016)
  20. Kimmig, Angelika; Mihalkova, Lilyana; Getoor, Lise: Lifted graphical models: a survey (2015)

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