Inductive Logic Programming
Description
- Project Title:
- Inductive Logic Programming
- Acronym:
- ILP
- Number:
- 6020
- Work Area:
- Machine Learning
- Coordinator:
- Katholieke Universiteit Leuven
Department of Computing Science
Celestijnlaan 200A
B - 3001 Heverlee
- Coordinator Country:
- B
- Partners
- Gesellschaft für Mathematik und Datenverarbeitung D
Universität Stuttgart D
CNRS - LRI F
Università di Torino I
University of Oxford UK
- Associate Partner
- University of Stockholm S
- Contact Point:
- Prof. Dr. Ir. M. Bruynooghe
- Telephone:
- +32/16 20 10 15
- Fax:
- +32/16 20 53 08
- E-Mail:
- maurice@cs.kuleuven.ac.be
- Keywords:
- induction, machine learning, inductive logic programming, logic programming, learning from example, knowledge-intensive learning
- Start Date:
- 1 September 92
- Duration:
- 36 months
- Status:
- running
- Abstract:
- Inductive logic programming (ILP) is the intersection of inductive learning and logic programming. The project focuses on the following research topics: theory of ILP, theory revision and multiple predicate learning, handling imperfect data, predicate invention, and declarative bias. The project will address theoretical issues and implementation of prototype learners and will carry out empirical evaluations.
AIMS
The main long term technical goal of the ILP project is to upgrade the techniques of the classical empirical learning paradigm to a logic programming framework. In this way ILP aims to overcome the two main limitations of classical empirical or similarity based learning algorithms, such as the TDIDT-family: the use of a limited knowledge representation formalism (essentially a propositional logic), and the inability to use substantial background knowledge in the learning process.
APPROACH AND METHODS
The project focuses on the following research topics:
- Theory of ILP: the theoretical implications of the use of logic programming for inductive learners. This involves the study of:
. the properties of generalisation and specialisation operators such as inverse resolution
. the complexity and convergence aspects of particular inductive algorithms (this is concerned with learnability theory)
. logical frameworks for induction
. the development of a framework and methodology for empirical evaluation of ILP-learners.
- Theory Revision: the issues involved in learning multiple concepts in a first-order logic framework. Learning multiple concepts is a form of theory revision, where several related predicates or concepts may be modified or revised.
- Imperfect data: to upgrade and adapt existing noise-handling mechanisms form attribute value learning algorithms.
- Predicate Invention: the investigation of methods to invent new predicates. These methods aim at extending the vocabulary of the learner whenever the available vocabulary is unsatisfactory or insufficient and by doing so they extend the range of learnable concepts.
- Declarative Bias: the exploration of methods and formalisms to explicitly and declaratively represent the bias of inductive logic learners.
PROGRESS AND RESULTS
Theoretical results obtained address:
- the (non)-pac-learnability of certain classes of logic programs
- the study of an alternative semantics or problem specification for ILP based on Helft's framework
Results in Theory revision includes:
- the development of multiple predicate learners in an incremental and empirical setting
- the study of minimal revisions to theories
- a technique to derive full clausal theories from deductive databases
A better understanding of predicate invention was obtained:
- by Muggleton's formal framework (and lattice) for predicate invention
- by introducing new techniques for predicate invention
- by comparative studies of predicate invention
Results on handling imperfect data include:
- adaptation of some mechanisms from attribute value learning to ILP
- development of stochastic ILP algorithms
- new results on information compression and Kolmogorov complexity
Results on declarative bias include:
- abstract frameworks for formulating bias and shifting the bias
- comparisons between existing frameworks for bias
- application of bias to programming assistants
POTENTIAL
The expected outcome of the project is a sound basis for the development of systems that are able to induce logic programs from examples in real-life applications that involve substantial amounts of background knowledge.
LATEST PUBLICATIONS
- Bergadano F and Gunetti D An interactive system to learn functinal logic programs in Proceedings of the 13th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, to appear (1993)
- Kietz J U Some lower bounds on the computational complexity of inductive logic programming, In: Brazdil P. (Ed) Proceedings of the 6th European Conference on Machine Learning, Lecture Notes in Artificial Intelligence 667, (1993)
- Lavrac N and Dzeroski S Inductive Logic Programming Techniques and Applications, Ellis Horwood, 1993, to appear
- Muggleton S Inverting Implication Artificial Intelligence, to appear
- De Raedt L and Bruynooghe M A theory of clausal discovery In: Proceedings of the 13th International joint Conference on Artificial Intelligence, Morgan Kaufmann, to appear (1993)
INFORMATION DISSEMINATION ACTIVITIES
Members of the project have given or will give invited talks and/or tutorials on inductive logic programming at major conferences and summerschools devoted to machine learning and/or logic programming (including ECML, ISMIS, ILPS, LLI, SCAI).
The third (Bled, 93) and fourth (Germany, 94) workshops on ILP are organised by partners in the project, and an IJCAI 93 workshop is devoted to ILP.
At the past European Conference on Machine Learning, about one third of the papers was related to ILP.
Further publications by the consortium will appear at major conferences (such as IJCAI, ICML) and major journals (AIJournal, JETAI, IEEE Transactions on Knowledge and Data Engineering, etc.)
Several ILP tools, technical reports and overviews of ILP can be obtained from the consortium.
Sven Müßig, last update 07-nov-1995. Your feedback is welcome.