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krkopt.info
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1. Title:
Chess Endgame Database for White King and Rook against Black King (KRK) -
Black-to-move Positions Drawn or Lost in N Moves.
2. Source Information:
-- Creators: Database generated by Michael Bain and Arthur van Hoff
at the Turing Institute, Glasgow, UK.
-- Donor: Michael Bain ([email protected]), AI Lab, Computer Science,
University of New South Wales, Sydney 2052, Australia.
(tel) +61 2 385 3939
(fax) +61 2 663 4576
-- Date: June, 1994.
3. Past Usage:
Chess endgames are complex domains which are enumerable. Endgame
databases are tables of stored game-theoretic values for the enumerated
elements (legal positions) of the domain. The game-theoretic values stored
denote whether or not positions are won for either side, or include also
the depth of win (number of moves) assuming minimax-optimal play. From the
point of view of experiments on computer induction such databases provide
not only a source of examples but also an oracle (Roycroft, 1986) for
testing induced rules. However a chess endgame database differs from, say,
a relational database containing details of parts and suppliers in the
following important respect. The combinatorics of computing the required
game-theoretic values for individual position entries independently would
be prohibitive. Therefore all the database entries are generated in a single
iterative process using the ``standard backup'' algorithm (Thompson, 1986).
A KRK database was described by Clarke (1977). The current database was
described and used for machine learning experiments in Bain (1992; 1994). It
should be noted that our database is not guaranteed correct, but the class
distribution is the same as Clarke's database. In (Bain 1992; 1994) the
task was classification of positions in the database as won for white in a
fixed number of moves, assuming optimal play by both sides. The problem was
structured into separate sub-problems by depth-of-win ordered draw, zero,
one, ..., sixteen. When learning depth d all examples at depths > d are
used as negatives. Quinlan (1994) applied Foil to learn a complete and
correct solution for this task.
The typical complexity of induced classifiers in this domain suggest
that the task is demanding when background knowledge is restricted.
4. Relevant Information:
An Inductive Logic Programming (ILP) or relational learning framework is
assumed (Muggleton, 1992). The learning system is provided with examples
of chess positions described only by the coordinates of the pieces on the
board. Background knowledge in the form of row and column differences is
also supplied. The relations necessary to form a correct and concise
classifier for the target concept must be discovered by the learning system
(the examples already provide a complete extensional definition).
The task is closely related to Quinlan's (1983) application of ID3 to
classify White King and Rook against Black King and Knight (KRKN) positions
as lost 2-ply or lost 3-ply. The framework is similar in that the example
positions supply only low-grade data. An important difference is that
additional background predicates of the kind supplied in the KRKN study via
hand-crafted attributes are not provided for this KRK domain.
5. Number of Instances: 28056
6. Number of Attributes:
There are six attribute variables and one class variable.
7. Attribute Information:
1. White King file (column)
2. White King rank (row)
3. White Rook file
4. White Rook rank
5. Black King file
6. Black King rank
7. optimal depth-of-win for White in 0 to 16 moves, otherwise drawn
{draw, zero, one, two, ..., sixteen}.
8. Missing Attribute Values: None
9. Class Distribution:
draw 2796
zero 27
one 78
two 246
three 81
four 198
five 471
six 592
seven 683
eight 1433
nine 1712
ten 1985
eleven 2854
twelve 3597
thirteen 4194
fourteen 4553
fifteen 2166
sixteen 390
Total 28056
10. Note: Foil is available by anonymous ftp from ftp.cs.su.oz.au, file
pub/foil6.sh.
References: (BibTeX format)
@incollection{bain_1992,
AUTHOR = "M. Bain",
TITLE = "Learning optimal chess strategies",
BOOKTITLE = "{ILP 92}: {P}roc. {I}ntl. {W}orkshop on
{I}nductive {L}ogic {P}rogramming",
YEAR = 1992,
VOLUME = "ICOT TM-1182",
EDITOR = "S. Muggleton",
PUBLISHER = "Institute for New Generation Computer Technology",
ADDRESS = "Tokyo, Japan"}
@phdthesis{bain_1994,
TITLE = "Learning {L}ogical {E}xceptions in {C}hess",
AUTHOR = "M. Bain",
SCHOOL = "University of Strathclyde",
YEAR = "1994"}
@incollection{clarke_1977,
AUTHOR = "M. R. B. Clarke",
TITLE = "A {Q}uantitative {S}tudy of {K}ing and {P}awn
{A}gainst {K}ing",
BOOKTITLE = "Advances in Computer Chess",
VOLUME = 1,
PAGES = "108--118",
EDITOR = "M. R. B. Clarke",
PUBLISHER = "Edinburgh University Press",
ADDRESS = "Edinburgh",
YEAR = "1977"}
@incollection{muggleton_1992,
AUTHOR = "S. Muggleton",
TITLE = "Inductive {L}ogic {P}rogramming",
BOOKTITLE = "Inductive {L}ogic {P}rogramming",
PAGES = "3--27",
EDITOR = "S. Muggleton",
PUBLISHER = "Academic Press",
ADDRESS = "London",
YEAR = "1992"}
@incollection{quinlan_1983,
AUTHOR = "J. R. Quinlan",
TITLE = "Learning {E}fficient {C}lassification {P}rocedures and their
{A}pplication to {C}hess {E}nd {G}ames",
YEAR = 1983,
PAGES = "464--482",
BOOKTITLE = "Machine Learning: An Artificial Intelligence
Approach",
EDITOR = "R. Michalski and J. Carbonnel and T. Mitchell",
PUBLISHER = "Tioga",
ADDRESS = "Palo Alto, CA"}
@misc{quinlan_1994,
AUTHOR = "J. R. Quinlan",
YEAR = 1994,
NOTE = "Personal Communication"}
@article{roycroft_1986,
AUTHOR = "A. J. Roycroft",
TITLE = "Database ``{O}racles'': {N}ecessary and desirable features",
JOURNAL = "International Computer Chess Association Journal",
YEAR = "1986",
VOLUME = 8,
NUMBER = 2,
PAGES = "100--104"}
@article{thompson_1986,
AUTHOR = "K. Thompson",
TITLE = "Retrograde {A}nalysis of {C}ertain {E}ndgames",
JOURNAL = "International Computer Chess Association Journal",
YEAR = "1986",
VOLUME = "8",
NUMBER = "3",
PAGES = "131--139"}