Name (abbrev)

Name (full)

Category

Last update

KRK

King-Rook-King (exact + noisy data)

Chess

b D, Y

Application domain

Further specifications

Learning Rules for King+Rook vs. King Endgame

set of datasets

Type

Format

Complexity

ILP

Golem

5 sets of 1000 examples each and 5 sets of 100 examples each. Variants of the latter with three different types and six different levels of noise

WWW / FTP

http://
http://

Contact person(s)

Related group(s)

Optional contact address

References

Dzeroski, S. (1991).
Handling noise in inductive logic programming.
MSc Thesis, Faculty of Electrical Engineering and Computer Science,
University of Ljubljana, Ljubljana.

Dzeroski, S. (1993).
Handling imperfect data in inductive logic programming.
In Proc. Fourth Scandinavian Conference on Artificial Intelligence,
pages 111--125, IOS Press, Amsterdam, 1993.

Lavrac, N., and Dzeroski, S. (1994).
Inductive Logic Programming: Techniques and Applications.
Ellis Horwood, Chichester.

Muggleton, S., Bain, M., Hayes-Michie, J., and Michie, D. (1989).
An experimental comparison of human and machine learning formalisms.
In Proc. Sixth International Workshop on Machine Learning,
pages 113--118. Morgan Kaufmann, San Mateo, CA.

Annotations

This directory contains data for the chess endgame White King and Rook vs. Black King (KRK). The primary dataset originates from [Muggleton et al. 89] reporting on experiments with 5 sets of 100 examples and 5 sets of1000 examples. The used language is decribed in the entry for dataset KRK: Learning Rules from Chess Databases (exact data).

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