1. Title: Kinship domain (relational)

2. Source Information
   -- Creator: Geoff Hinton
   -- Donor: J. Ross Quinlan
   -- Date: July 1990
 
3. Past Usage:
    1. Hinton, G.E (1986). Learning distributed representations of concepts,
       Proceedings of CogSci 1986.
    2. Quinlan, J.R. (1989).  Learning relations: Comparison of a symbolic
       and a connectionist approach.  Predecessor of Ross's article in an
       upcoming Machine Learning issue.

4. Relevant Information:
   -- This relational database consists of 24 unique names in two families
      (they have equivalent structures).  Hinton used one unique output
      unit for each person and was interested in predicting the following
      relations: wife, husband, mother, father, daughter, son, sister, brother,
      aunt, uncle, niece, and nephew.  Hinton used 104 input-output vector
      pairs (from a space of 12x24=288 possible pairs).  The prediction task
      is as follows: given a name and a relation, have the outputs be on for
      only those individuals (among the 24) that satisfy the relation.  The
      outputs for all other individuals should be off.
   -- Hinton's results: Using 100 vectors as input and 4 for testing, his 
      results on two passes yielded 7 correct responses out of 8.  His network
      of 36 input units, 3 layers of hidden units, and 24 output units used
      500 sweeps of the training set during training.
   -- Quinlan's results: Using FOIL, he repeated the experiment 20 times 
      (rather than Hinton's 2 times).  FOIL was correct 78 out of 80 times on
      the test cases.  

5. Number of Instances: 104 generated from the relations shown in the database.

6. Number of Attributes: 12 possible binary relations 

7. Attribute Information:
   -- The relation names are:
      wife
      husband
      mother
      father
      daughter
      son
      sister
      brother
      aunt
      uncle
      niece
      nephew

8. Missing Attribute Values: None