1. Title: Student Loan Relational Domain

2. Sources: 
   (a) Donors: Michael J. Pazzani 
               University of California, Irvine
               Irvine, CA USA
   (b) Date:   January 1993

3. Past Usage:

   Pazzani, M., & Brunk, C. (1991). Detecting and correcting errors in
   rule-based expert systems: an integration of empirical and explanation-based
   learning. Knowledge Acquisition, 3, 157-173.

4. Relevant Information:

   The predicate no_payment_due/1 is true for those people who are
   not required to repay a student loan.  Auxiliary relations 
   can be used to fully discriminate positive from negative
   instances of no_payment_due/1.  Closed world assumption applies
   to all auxiliary relations.

5. Target Relation:

   - no_payment_due(Person)

   - Number of positive instances: 643

   - Negative instances correspond to the relation: 

       - payment_due(Person).

       - Number of negative instances: 357
  

6. Auxiliary Relations:

   male(Person).
   longest_absense_from_school(Person, Number_of_Months).
   enrolled(Person,School,Units).
   enlist(Person,Organization).
   unemployed(Person).
   filed_for_bankrupcy(Person).
   disabled(Person).
   school(School).
   armed_forces(Organization).
   peace_corps(Organization).