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74 lines
3.2 KiB
Plaintext
Executable File
74 lines
3.2 KiB
Plaintext
Executable File
1. Title: Tic-Tac-Toe Endgame database
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2. Source Information
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-- Creator: David W. Aha (aha@cs.jhu.edu)
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-- Donor: David W. Aha (aha@cs.jhu.edu)
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-- Date: 19 August 1991
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3. Known Past Usage:
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1. Matheus,~C.~J., \& Rendell,~L.~A. (1989). Constructive
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induction on decision trees. In {\it Proceedings of the
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Eleventh International Joint Conference on Artificial Intelligence}
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(pp. 645--650). Detroit, MI: Morgan Kaufmann.
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-- CITRE was applied to 100-instance training and 200-instance test
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sets. In a study using various amounts of domain-specific
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knowledge, its highest average accuracy was 76.7% (using the
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final decision tree created for testing).
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2. Matheus,~C.~J. (1990). Adding domain knowledge to SBL through
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feature construction. In {\it Proceedings of the Eighth National
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Conference on Artificial Intelligence} (pp. 803--808).
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Boston, MA: AAAI Press.
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-- Similar experiments with CITRE, includes learning curves up
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to 500-instance training sets but used _all_ instances in the
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database for testing. Accuracies reached above 90%, but specific
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values are not given (see Chris's dissertation for more details).
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3. Aha,~D.~W. (1991). Incremental constructive induction: An instance-based
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approach. In {\it Proceedings of the Eighth International Workshop
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on Machine Learning} (pp. 117--121). Evanston, ILL: Morgan Kaufmann.
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-- Used 70% for training, 30% of the instances for testing, evaluated
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over 10 trials. Results reported for six algorithms:
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-- NewID: 84.0%
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-- CN2: 98.1%
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-- MBRtalk: 88.4%
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-- IB1: 98.1%
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-- IB3: 82.0%
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-- IB3-CI: 99.1%
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-- Results also reported when adding an additional 10 irrelevant
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ternary-valued attributes; similar _relative_ results except that
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IB1's performance degraded more quickly than the others.
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4. Relevant Information:
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This database encodes the complete set of possible board configurations
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at the end of tic-tac-toe games, where "x" is assumed to have played
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first. The target concept is "win for x" (i.e., true when "x" has one
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of 8 possible ways to create a "three-in-a-row").
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Interestingly, this raw database gives a stripped-down decision tree
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algorithm (e.g., ID3) fits. However, the rule-based CN2 algorithm, the
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simple IB1 instance-based learning algorithm, and the CITRE
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feature-constructing decision tree algorithm perform well on it.
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5. Number of Instances: 958 (legal tic-tac-toe endgame boards)
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6. Number of Attributes: 9, each corresponding to one tic-tac-toe square
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7. Attribute Information: (x=player x has taken, o=player o has taken, b=blank)
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1. top-left-square: {x,o,b}
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2. top-middle-square: {x,o,b}
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3. top-right-square: {x,o,b}
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4. middle-left-square: {x,o,b}
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5. middle-middle-square: {x,o,b}
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6. middle-right-square: {x,o,b}
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7. bottom-left-square: {x,o,b}
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8. bottom-middle-square: {x,o,b}
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9. bottom-right-square: {x,o,b}
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10. Class: {positive,negative}
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8. Missing Attribute Values: None
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9. Class Distribution: About 65.3% are positive (i.e., wins for "x")
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