# Info on Tic-Tac-Toe Endgame Data Base

    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:
       1. 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).

       2. 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).

       3. 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.

    4. 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.

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

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

    7. 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}
       10. Class: {positive,negative}

    8. Missing Attribute Values: None

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