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 55 articles )

Showing results 1 to 20 of 55.
Sorted by year (citations)

1 2 3 next

  1. Cropper, Andrew; Dumančić, Sebastijan; Evans, Richard; Muggleton, Stephen H.: Inductive logic programming at 30 (2022)
  2. Dash, Tirtharaj; Srinivasan, Ashwin; Baskar, A.: Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment (2022)
  3. Cropper, Andrew; Morel, Rolf: Learning programs by learning from failures (2021)
  4. Dash, Tirtharaj; Srinivasan, Ashwin; Vig, Lovekesh: Incorporating symbolic domain knowledge into graph neural networks (2021)
  5. Furelos-Blanco, Daniel; Law, Mark; Jonsson, Anders; Broda, Krysia; Russo, Alessandra: Induction and exploitation of subgoal automata for reinforcement learning (2021)
  6. Ramanan, Nandini; Kunapuli, Gautam; Khot, Tushar; Fatemi, Bahare; Kazemi, Seyed Mehran; Poole, David; Kersting, Kristian; Natarajan, Sriraam: Structure learning for relational logistic regression: an ensemble approach (2021)
  7. Bekker, Jessa; Davis, Jesse: Learning from positive and unlabeled data: a survey (2020)
  8. Cropper, Andrew; Evans, Richard; Law, Mark: Inductive general game playing (2020)
  9. Cropper, Andrew; Morel, Rolf; Muggleton, Stephen: Learning higher-order logic programs (2020)
  10. Lavrač, Nada; Škrlj, Blaž; Robnik-Šikonja, Marko: Propositionalization and embeddings: two sides of the same coin (2020)
  11. Shakerin, Farhad; Gupta, Gopal: White-box induction from SVM models: explainable AI with logic programming (2020)
  12. Srinivasan, Ashwin; Vig, Lovekesh; Shroff, Gautam: Constructing generative logical models for optimisation problems using domain knowledge (2020)
  13. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  14. Michelioudakis, Evangelos; Artikis, Alexander; Paliouras, Georgios: Semi-supervised online structure learning for composite event recognition (2019)
  15. Nguembang Fadja, Arnaud; Riguzzi, Fabrizio: Lifted discriminative learning of probabilistic logic programs (2019)
  16. Shakerin, Farhad: Induction of non-monotonic logic programs to explain statistical learning models (2019)
  17. Srinivasan, Ashwin; Vig, Lovekesh; Bain, Michael: Logical explanations for deep relational machines using relevance information (2019)
  18. 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)
  19. Dutta, Haimonti; Srinivasan, Ashwin: Consensus-based modeling using distributed feature construction with ILP (2018)
  20. Law, Mark; Russo, Alessandra; Broda, Krysia: The complexity and generality of learning answer set programs (2018)

1 2 3 next