A Multi-Level Organization for Problem Solving Using Many, Diverse, Cooperating Sources of Knowledge

Abstract

An organization is presented for implementing solutions to knowledge- based Artificial Intelligence problems. The hypothesize-and-test paradigm is used as the basis for cooperation among many diverse and independent knowledge sources (KS's). The KS's are assumed individually to be errorful and incomplete. A uniform and integrated multi-level structure, the blackboard holds the current state of the system. Knowledge sources cooperate by creating, accessing, and modifying elements in the blackboard. Each level in the blackboard specifies a different representation of the problem space; the sequence of levels forms a loose hierarchy in which the elements at each level can approximately be described as abstractions of elements at the next lower level. The elements at each level in the blackboard are hypotheses about some aspect of that level. The internal structure of an hypothesis consists of a fixed set of attributes; this set is the same for hypotheses at all levels of representation in the blackboard. These attributes are selected to serve as mechanisms for implementing the data-directed hypothesize-and-test paradigm and for efficient goal-directed scheduling of KS's. The HearsayII speech- understanding system is an implementation of this organization; it is used here as an example for descriptive purposes.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1975
Accession Number
ADA012916

Entities

People

  • Lee D. Erman
  • Victor R. Lesser

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acoustic Signals
  • Acoustic Waves
  • Acoustics
  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Automated Speech Recognition
  • Classification
  • Cognition
  • Computer Science
  • Databases
  • Language
  • Models
  • Recognition
  • Signal Processing
  • Test And Evaluation
  • Universities

Readers

  • Artificial Intelligence

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space