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+1. Title: The Monk's Problems
+
+2. Sources: 
+    (a) Donor: Sebastian Thrun
+	       School of Computer Science
+	       Carnegie Mellon University
+	       Pittsburgh, PA 15213, USA
+
+	       E-mail: thrun@cs.cmu.edu
+
+    (b) Date: October 1992
+
+3. Past Usage: 
+
+   - See File: thrun.comparison.ps.Z
+
+   - Wnek, J., "Hypothesis-driven Constructive Induction," PhD dissertation, 
+     School of Information Technology and Engineering, Reports of Machine 
+     Learning and Inference Laboratory, MLI 93-2, Center for Artificial 
+     Intelligence, George Mason University, March 1993.
+
+   - Wnek, J. and Michalski, R.S., "Comparing Symbolic and 
+     Subsymbolic Learning: Three Studies," in Machine Learning: A 
+     Multistrategy Approach, Vol. 4., R.S. Michalski and G. Tecuci (Eds.), 
+     Morgan Kaufmann, San Mateo, CA, 1993.
+
+4. Relevant Information:
+
+   The MONK's problem were the basis of a first international comparison
+   of learning algorithms. The result of this comparison is summarized in
+   "The MONK's Problems - A Performance Comparison of Different Learning
+   algorithms" by S.B. Thrun, J. Bala, E. Bloedorn, I.  Bratko, B.
+   Cestnik, J. Cheng, K. De Jong, S.  Dzeroski, S.E. Fahlman, D. Fisher,
+   R. Hamann, K. Kaufman, S. Keller, I. Kononenko, J.  Kreuziger, R.S.
+   Michalski, T. Mitchell, P.  Pachowicz, Y. Reich H.  Vafaie, W. Van de
+   Welde, W. Wenzel, J. Wnek, and J. Zhang has been published as
+   Technical Report CS-CMU-91-197, Carnegie Mellon University in Dec.
+   1991.
+
+   One significant characteristic of this comparison is that it was
+   performed by a collection of researchers, each of whom was an advocate
+   of the technique they tested (often they were the creators of the
+   various methods). In this sense, the results are less biased than in
+   comparisons performed by a single person advocating a specific
+   learning method, and more accurately reflect the generalization
+   behavior of the learning techniques as applied by knowledgeable users.
+
+   There are three MONK's problems.  The domains for all MONK's problems
+   are the same (described below).  One of the MONK's problems has noise
+   added. For each problem, the domain has been partitioned into a train
+   and test set.
+
+5. Number of Instances: 432
+
+6. Number of Attributes: 8 (including class attribute)
+
+7. Attribute information:
+    1. class: 0, 1 
+    2. a1:    1, 2, 3
+    3. a2:    1, 2, 3
+    4. a3:    1, 2
+    5. a4:    1, 2, 3
+    6. a5:    1, 2, 3, 4
+    7. a6:    1, 2
+    8. Id:    (A unique symbol for each instance)
+
+8. Missing Attribute Values: None
+
+9. Target Concepts associated to the MONK's problem:
+
+   MONK-1: (a1 = a2) or (a5 = 1)
+
+   MONK-2: EXACTLY TWO of {a1 = 1, a2 = 1, a3 = 1, a4 = 1, a5 = 1, a6 = 1}
+
+   MONK-3: (a5 = 3 and a4 = 1) or (a5 /= 4 and a2 /= 3)
+           (5% class noise added to the training set)
+