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 33 articles )

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  1. Paes, Aline; Zaverucha, Gerson; Costa, Vítor Santos: On the use of stochastic local search techniques to revise first-order logic theories from examples (2017)
  2. Srinivasan, Ashwin; Bain, Michael: An empirical study of on-line models for relational data streams (2017)
  3. Adhikari, Prem Raj; Vavpetič, Anže; Kralj, Jan; Lavrač, Nada; Hollmén, Jaakko: Explaining mixture models through semantic pattern mining and banded matrix visualization (2016)
  4. Angelopoulos, Nicos; Abdallah, Samer; Giamas, Georgios: Advances in integrative statistics for logic programming (2016)
  5. Kaalia, Rama; Srinivasan, Ashwin; Kumar, Amit; Ghosh, Indira: ILP-assisted de novo drug design (2016) ioport
  6. Law, Mark; Russo, Alessandra; Broda, Krysia: Iterative learning of answer set programs from context dependent examples (2016)
  7. Kimmig, Angelika; Mihalkova, Lilyana; Getoor, Lise: Lifted graphical models: a survey (2015)
  8. Muggleton, Stephen H.; Lin, Dianhuan; Tamaddoni-Nezhad, Alireza: Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015)
  9. Farid, Reza; Sammut, Claude: Plane-based object categorisation using relational learning (2014) ioport
  10. Frasconi, Paolo; Costa, Fabrizio; De Raedt, Luc; De Grave, Kurt: kLog: a language for logical and relational learning with kernels (2014)
  11. Barták, Roman; Černoch, Radomír; Kuželka, Ondřej; Železný, Filip: Formulating the template ILP consistency problem as a constraint satisfaction problem (2013)
  12. Natarajan, Sriraam; Khot, Tushar; Kersting, Kristian; Gutmann, Bernd; Shavlik, Jude: Gradient-based boosting for statistical relational learning: the relational dependency network case (2012)
  13. Srinivasan, Ashwin; Faruquie, Tanveer A.; Joshi, Sachindra: Data and task parallelism in ILP using mapreduce (2012)
  14. Kalgi, Srihari; Gosar, Chirag; Gawde, Prasad; Ramakrishnan, Ganesh; Gada, Kekin; Iyer, Chander; Kiran, T.V.S.; Srinivasan, Ashwin: BET: An inductive logic programming workbench (2011) ioport
  15. Muggleton, Stephen; Santos, José; Tamaddoni-Nezhad, Alireza: ProGolem: a system based on relative minimal generalisation (2010)
  16. Santos, Jose; Muggleton, Stephen: Subsumer: a Prolog $\theta$-subsumption engine (2010)
  17. Duboc, Ana Luísa; Paes, Aline; Zaverucha, Gerson: Using the bottom clause and mode declarations in FOL theory revision from examples (2009) ioport
  18. Ray, Oliver: Nonmonotonic abductive inductive learning (2009)
  19. Specia, Lucia; Srinivasan, Ashwin; Joshi, Sachindra; Ramakrishnan, Ganesh; Nunes, Maria Das Graças Volpe: An investigation into feature construction to assist word sense disambiguation (2009) ioport
  20. Tamaddoni-Nezhad, Alireza; Muggleton, Stephen: The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause (2009) ioport

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