BK\webpage R&HML in Finance\ILP index

Langley, P. 1995. Elements of Machine Learning

Machine Learning, Textbook,1997, T.Mitchell

Machine Learning, T. Mitchell
Machine Learning, S. Salzberg
Logic Programming and Learning Course , S. Muggleton,C.D. Page and A. Srinivasan
Machine Learning, R Rivest
Machine Learning, Brodley

Application of Clausal Discovery to Temporal Databases, David Lorenzo

Relational Knowledge Discovery in Databases, Hendrik Blockeel and Luc De Raedt
A Bi-directional ILP Algorithm, Markus Wiese
Direct Access of an ILP Algorithm to a Database Management System, Peter Brockhausen and Katharina Morik
Handling real numbers in Inductive Logic Programming: a step towards better behavioural clones
Predicate Invention in ILP (bibliography)
Predicate Invention for Numerical Data (Representative Measurement theory)

S. Muggleton. A strategy for constructing new predicates in first order logic. In Proceedings of the Third European Working Session on Learning, pages 123-130. Pitman, 1988

Bratko, I., Muggleton. S. Applications of inductive logic programming, Communications of ACM, vol.38, N. 11,1995, pp.65-70 (access ACM members, pdf)

Learning First-Order Acyclic Horn Program from Entailment, C. Reddy and P. Tadepalli (ps)

Computational Scientific Discovery

Machine Discovery

F. Bergadano and D. Gunetti.Relational Machine Learning and the Inductive Synthesis of Logic Programs.

In M. di Bacco, E. Pacciani and S. Borgognini Tarli, Eds.,Statistical Tools in Human Biology. World Scientific Publishing Co.,1994.

(Propositional) Rules are Much More than Decision Trees , Parsaye, K.,The Journal of Data Warehousing, January 1997. While rules and decision trees may seem similar at first, they are in fact very different. This paper discusses how and why rules are so much more powerful than decision trees. It provides simple examples that illustrates how decision trees miss key patterns that rules find and how prediction with decision trees may at times lead to random results.
A comparison of ILP and propositional systems on propositional data. S. Roberts, W. Van Laerand, N. Jacobs, S. Muggleton, and J. Broughton. In C.D. Page, editor, Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), Berlin, 1998. Springer-Verlag. To appear.

ILPNET systems
Golem
FOIL
M-FOIL for noise
Learning Relations by Pathfinding Bradley L. Richards and Raymond J. Mooney Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 50-55, San Jose, CA, July 1992. First-order learning systems (e.g. FOIL, FOCL, FORTE) generally rely on hill-climbing heuristics in order to avoid the combinatorial explosion inherent in learning first-order concepts. However, hill-climbing leaves these systems vulnerable to local maxima and local plateaus. Presented a method called relational pathfinding, which has proven highly effective in escaping local maxima and crossing local plateaus.

Decision trees and first-order logic methods, Ross Quinlan

Cubist (Composite Models: rule-based model combined with an instance-based or nearest-neighbor model), Ross Quinlan

Siftware

Prolog

Public-domain, free Prolog for the IBM PC, the Mac and Unix

Directory of /pub/SWI-Prolog

ILPNET, the Inductive Logic Programming Pan-European Scientific Network

ILPnet2

Center for Automated Learning and Discovery, Carnegie Mellon University

Oxford University ILP webpage

Representative Measurement theory, R.D. Luce

Abstract measurement Theory, L. Narens.