Commit Inicial

data/tanveer/tic-tac-toe/tic-tac-toe.names

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+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")