Contents and links
Introduction: learning about endgames
Early work on propositional learning from logic databases such as those
provided by chess endgames played a key role in highlighting the easy-inverse
trick which was later used in developing systems such as Kardio [Bratko
I., Mozetic I. and Lavrac N. (1989)]. These databases are good sources
of examples for both learning and testing rules. In fact relatively simple
chess problems have in the past defeated Machine Learning systems which
have otherwise proved rather successful on a range of real-world tasks.
Recognising illegal positions
The data provided here concerns one such simple problem: that of the KRK
endgame. In this, there are three pieces left on the chess board: White
King, White Rook and Black King. The problem of learning rules for recognising
illegal positions when it is white's turn to move (WTM) was first proposed
by [Muggleton S.H., Bain M.E., Hayes-Michie J. and Michie
D. (1989)]. This has since become a widely accepted test-bed for ILP
systems.
An example of an illegal position is shown below:
This is illegal since BK:e1 is in check with WTM. The following example,
in contrast, shows a legal position: 
.
ILP systems usually use the following information to learn the concept:
Examples are represented by the predicate illegal/6. The arguments
of this predicate stand for the file (column) and rank (row) coordinates
for the WK, WR and BK.
The background knowledge provided is simply the ordering of rows or columns
on a chess board. The predicate lt/2 tabulates pairs row or column
values where one is less than the other. The predicate adj/2 tabulates
row or column values that are adjacent.
Predicting optimal depth of win
Another, more difficult problem is to do with predicting optimal depth
of win (that is, the number of moves to checkmate). In the data here, the
exhaustive database for the KRK domain acts as a source of example positions,
each of which has associated with it optimal depth of win information.
Here this is represented by the predicate krk/7. The arguments
for this predicate stand for depth of win, and the file (column) and rank
(row) coordinates for the WK, WR and BK. The typical ILP task is to learn
the sub-concept `black-to-move KRK position won optimally for white in
N moves''. Preliminary results of classifiers for ``won in 0 moves'' up
to ``won in 5 moves'', can be found in >[Bain M. and
Srinivasan A. (1995)]. The background knowledge used consisted of ground
instances of the predicates ``symmetric difference'' between files and
ranks and ``strictly less than'' (&;lt;) for all unordered pairs of
the file values a...h and all unordered pairs of the rank values 1...8.
The Golem datasets
The data files are held in one
compressed TAR file, containing predicates as described above. Within
the TAR file, they are as used in the original Golem experiments. That
is, background knowledge files have a ``.b'' suffix, positive example files
have a ``.f'' suffix, and negative example files have a ``.n'' suffix.
Bibliography
Bain M. and Srinivasan A. (1995).
Inductive Logic Programming With Large-Scale Unstructured Data.
In D. Michie K. Furukawa and S. Muggleton, editors,
Machine Intelligence 14. Oxford University Press.
Bratko I., Mozetic I. and Lavrac N. (1989).
Kardio: a Study in Deep and Qualitative Knowledge for Expert Systems.
MIT Press, Cambridge Mass.
Muggleton S.H., Bain M.E., Hayes-Michie
J. and Michie D. (1989).
An experimental comparison of human and machine learning formalisms.
Proceedings of the Sixth International Workshop on Machine Learning
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