Supporting Organizational Problem Solving with a Workstation.

Abstract

This paper describes an approach to supporting work in the office. Using and extending ideas from the field of Artificial Intelligence (AI), we describe office work as a problem solving activity. A knowledge-embedding language called Omega is used to embed knowledge of the organization into an office worker's workstation in order to support the office worker in his or her problem solving. A particular approach to reasoning about change and contradiction is discussed. This approach uses Omega's viewpoint mechanism. Omega's viewpoint mechanism is a general contradiction handling facility. Unlike other knowledge Representation systems, when a contradiction is reached the reasons for the contradiction can be analyzed by the deduction mechanism without having to resort to a backtracking mechanism. The Viewpoint mechanism is the heart of the Problem Solving Support Paradigm. This paradigm supplements the classical AI view of problem solving. Office workers are supported using the Problem Solving Support Paradigm. An example is provided which illustrates the use of viewpoints and of Omega's capabilities to reason about its own reasoning process.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1982
Accession Number
ADA130481

Entities

People

  • Gerald Barber

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cognition
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Embedding
  • Information Systems
  • Language
  • Massachusetts
  • Organizational Structure
  • Personality
  • Programming Languages
  • Reasoning
  • Semantics
  • Thinking

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Linear Algebra
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms