A Paradigm for Efficient Subset Recognition

Abstract

The notion of a finite-state automation is examined with regard to problems involving the recognition of sets rather than strings. More precisely, this paper is concerned with the application of the methodology of automata theory to the general problem of efficiently determining whether a set A (or possible a subset of A) is a subset of a set of sets S. The following contributions result from this examination: (1) The traditional notion of a finite-state automation is shown to be impractical for use in set recognition; (2) Set-expressions, which are roughly analogous to regular expressions, are formally defined as a notation for describing member-qualified sets or classes of sets; (3) A set-recognizing automation (SRA) is formally defined and an algorithm is presented for its construction from a given set-expression; and (4) More powerful forms of SRA's; are developed which may have important practical applications in the area of artificial intelligence. In particular, the subset machine is developed for the fast processing of a restricted class of IF-THEN rules. Algorithm descriptions as well as LISP routines are provided. (Author) (kr)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1990
Accession Number
ADA227694

Entities

People

  • Jeffrey Uhlmann

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Automata
  • Automata Theory
  • Automation
  • Computations
  • Computer Science
  • Construction
  • Equations
  • Expert Systems
  • Gap Analysis
  • Inference Engines
  • Machines
  • Mathematical Analysis
  • Notation
  • Recognition
  • Standards

Readers

  • Artificial Intelligence
  • Mathematical Modeling and Probability Theory.

Technology Areas

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