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arff slurped Weka

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# Instances: 958 / # Attributes: 10
HDF5 (393.8 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

tic-tac-toe

Version mldata

0

Comment

1. Title: Tic-Tac-Toe Endgame database

2. Source Information -- Creator: David W. Aha (aha@cs.jhu.edu) -- Donor: David W. Aha (aha@cs.jhu.edu) -- Date: 19 August 1991

3. Known Past Usage:

4. Matheus,~C.~J., & Rendell,~L.~A. (1989). Constructive induction on decision trees. In {it Proceedings of the Eleventh International Joint Conference on Artificial Intelligence} (pp. 645--650). Detroit, MI: Morgan Kaufmann. -- CITRE was applied to 100-instance training and 200-instance test sets. In a study using various amounts of domain-specific knowledge, its highest average accuracy was 76.7% (using the final decision tree created for testing).

5. Matheus,~C.~J. (1990). Adding domain knowledge to SBL through feature construction. In {it Proceedings of the Eighth National Conference on Artificial Intelligence} (pp. 803--808). Boston, MA: AAAI Press. -- Similar experiments with CITRE, includes learning curves up to 500-instance training sets but used all instances in the database for testing. Accuracies reached above 90%, but specific values are not given (see Chris's dissertation for more details).

6. Aha,~D.~W. (1991). Incremental constructive induction: An instance-based approach. In {it Proceedings of the Eighth International Workshop on Machine Learning} (pp. 117--121). Evanston, ILL: Morgan Kaufmann. -- Used 70% for training, 30% of the instances for testing, evaluated over 10 trials. Results reported for six algorithms: -- NewID: 84.0% -- CN2: 98.1%
   -- MBRtalk: 88.4% -- IB1: 98.1% -- IB3: 82.0% -- IB3-CI: 99.1% -- Results also reported when adding an additional 10 irrelevant ternary-valued attributes; similar relative results except that IB1's performance degraded more quickly than the others.

7. Relevant Information:

This database encodes the complete set of possible board configurations at the end of tic-tac-toe games, where "x" is assumed to have played first. The target concept is "win for x" (i.e., true when "x" has one of 8 possible ways to create a "three-in-a-row").

Interestingly, this raw database gives a stripped-down decision tree algorithm (e.g., ID3) fits. However, the rule-based CN2 algorithm, the simple IB1 instance-based learning algorithm, and the CITRE feature-constructing decision tree algorithm perform well on it.

1. Number of Instances: 958 (legal tic-tac-toe endgame boards)

2. Number of Attributes: 9, each corresponding to one tic-tac-toe square

3. Attribute Information: (x=player x has taken, o=player o has taken, b=blank)

   1. top-left-square: {x,o,b}
   2. top-middle-square: {x,o,b}
   3. top-right-square: {x,o,b}
   4. middle-left-square: {x,o,b}
   5. middle-middle-square: {x,o,b}
   6. middle-right-square: {x,o,b}
   7. bottom-left-square: {x,o,b}
   8. bottom-middle-square: {x,o,b}
   9. bottom-right-square: {x,o,b}

4. Class: {positive,negative}

5. Missing Attribute Values: None

6. Class Distribution: About 65.3% are positive (i.e., wins for "x")

Information about the dataset CLASSTYPE: nominal CLASSINDEX: last

Names

top-left-square,top-middle-square,top-right-square,middle-left-square,middle-middle-square,middle-right-square,bottom-left-square,bottom-middle-square,bottom-right-square,Class,

Types

1. nominal:b,o,x
2. nominal:b,o,x
3. nominal:b,o,x
4. nominal:b,o,x
5. nominal:b,o,x
6. nominal:b,o,x
7. nominal:b,o,x
8. nominal:b,o,x
9. nominal:b,o,x
10. nominal:negative,positive

Data (first 10 data points)

top-...	top-...	top-...	midd...	midd...	midd...	bott...	bott...	bott...	Class
x	x	x	x	o	o	x	o	o	posi...
x	x	x	x	o	o	o	x	o	posi...
x	x	x	x	o	o	o	o	x	posi...
x	x	x	x	o	o	o	b	b	posi...
x	x	x	x	o	o	b	o	b	posi...
x	x	x	x	o	o	b	b	o	posi...
x	x	x	x	o	b	o	o	b	posi...
x	x	x	x	o	b	o	b	o	posi...
x	x	x	x	o	b	b	o	o	posi...
x	x	x	x	b	o	o	o	b	posi...
...	...	...	...	...	...	...	...	...	...

Description

A gzip'ed tar containing UCI and UCI KDD datasets (uci-20070111.tar.gz, 17,952,832 Bytes)

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Data Source

http://www.ics.uci.edu/~mlearn/MLRepository.html http://kdd.ics.uci.edu/

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revision 1

by mldata on 2010-11-06 09:58

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