Experiments in Automatic Learning for a Multipurpose Heuristic Program

An automatic learning capability has been developed
and implemented for use with the MULTIPLE 
(MULTIpurpose Program that LEarns) heuristic tree-searching
program, which is presently being applied 
to resolution theorem-proving in predicate calculus.
MULTIPLE's proving program (PP) uses two evaluation 
functions to guide its search for a proof of whether
or not a particular goal is achievable.  Thirteen 
general features of predicate calculus clauses were created
for use in the automatic learning of better 
evaluation functions for PP.  A multiple regression
program was used to produce optimal coefficients 
for linear polynomial functions in terms of the features.
 Also, automatic data-handling routines were 
written for passing data between the learning program
and the proving program, and for analyzing and 
summarizing results.  Data was generally collected for
learning (regression analysis) from the experience 
of PP.  A number of experiments were performed to test
the effectiveness and generality of the learning 
program. Results showed that the learning produced dramatic
improvements in the solutions to problems 
which were in the same domain as those used for collection
learning data.  Learning was also shown to 
generalize successfully to domains other than those used
for data collection.  Another experiment demonstrated 
that the learning program could simultaneously improve
performance on problems in a specific domain and 
on problems in a variety of domains.  Some variations
of the learning program were also tested.

CACM February, 1971

Slagle, J. R.
Farrell, C. D.

learning, theorem-providing, heuristic, automatic
learning, self-modifying,tree-searching, artificial 
intelligence, problem-solving, adaptive, LISP, multiple regression, resolution

3.62 3.64

CA710204 JB February 8, 1978  9:33 AM

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