Investigating Production System Representations for Non-Combinatorial Match

Abstract

Eliminating combinatorics from the match in production systems (or rule-based systems) is important for expert systems, real-time performance, machine learning (particularly with respect to the utility issue), parallel implementations and cognitive modeling. In (71) the unique-attributes engender a sufficiently negative set of trade-offs, so that investigating whether there are alternative. Representations that yield better trade-offs becomes of critical importance. This article identifies two promising spaces of such alternatives, and explores a number of the alternatives within these spaces. The first is generated from local syntactic restrictions on working memory. Within this pace, unique-attributes is shown to be the best alternative possible. The second space comes from restrictions on the search performed during the match of individual productions (match-search). In particular, this space is derived from the combination of a new, more relaxed, match formulation (instantiationless match) and a set of restrictions derived from the constraint-satisfaction literature. Within this space, new alternatives are found that outperform unique-attributes in some, but not yet all domains

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1993
Accession Number
ADA269767

Entities

People

  • Milind Tambe
  • Paul Simon Rosenbloom

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Availability
  • California
  • Computations
  • Contracts
  • Expert Systems
  • Information Science
  • Language
  • Learning
  • Literature
  • Machine Learning
  • Production
  • Rule Based Systems
  • Standards
  • Trees (Data Structures)

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Life Cycle Cost Analysis
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Space