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The History of Artificial Intelligence

Total complexity and the inference of best programs.

purl.stanford.edu/bf090tb3193
Title:
Total complexity and the inference of best programs.
Author:
Feldman, Jerome A.
Author (no Collectors):
Feldman, Jerome A.
Description:
Axioms for a total complexity measure for abstract programs are presented. Essentially, they require that total complexity be an unbounded increasing function of the Blum time and size measures. Algorithms for finding the best program on a finite domain are presented, and their limiting behaviour for infinite domains described. For total complexity, there are important senses in which a machine $underline{can}$ find the best program for a large class of functions.
Topic:
Computer science
Subject:
Stanford University. Computer Science Department
Language:
English
Physical Description:
1 text file
Publication Info:
cau and Stanford (Calif.)
Date:
April 01, 1972
Place created:
Stanford (Calif.)
Imprint:
Stanford (Calif.), April 1, 1972
Genre:
technical reports
Identifier:
CS-TR-1972-253
Repository:
Stanford University. Libraries. Department of Special Collections and University Archives
Collection:
Stanford University, Department of Computer Science, Technical Reports and Stanford Artificial Intelligence Laboratory records, 1963-2009
Manuscript number:
3840/2