STRATEGIES OF FUNCTION DECOMPOSITION FOR ARTIFICIAL INTELLIGENCE, VOLUME II.

Abstract

Preliminary results are reported in 13 research notes on strategies of function decomposition solely from observations of inputs (variable configurations) and outputs (function values). The classes of functions to which the results apply include discrete, finite, deterministic functions as well as arbitrary close discrete approximations to continuous functions of continuous variables. Nondeterministic (i.e., probabilistic) and sequential (i.e., finite automata) functions are not included. The research notes consider (a) decomposition costs and the equivalence of all measures of cost or complexity; the detection of economical decompositions; and (c) generalizing properties of economical decompositions. Efficient procedures are suggested for detecting economical non-composite decompositions of any given partial or total discrete function solely from input and output observations. Composite decompositions become tractable when enough is known or properly conjectured about their sub-functions. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1965
Accession Number
AD0620186

Entities

People

  • David G. Willis

Tags

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Automata
  • Composite Materials
  • Decomposition
  • Detection
  • Observation

Readers

  • Mathematical Modeling and Probability Theory.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms