Evaluation of Expert Systems in Decisionmaking Organizations

Abstract

A class of decision aids that is receiving attention in the development community is based on artificial intelligence and especially expert systems. This paper presents a procedure for assessing to what extent the measures of performance of an organization are modified when an expert system is introduced. First, a model of symbolic computation with fuzzy logic, using Predicate Transition Nets, is presented to model the most common kind of expert systems the consultant expert systems. This model allows to evaluate its response time for a given input. An Air Defense problem in which command and control involves a hierarchical two decisionmaker organization, where the expert system is used as an aid in the fusion of inconsistent information, is then presented. A strategy involving the use of the expert system is compared to two other strategies expected to be used by a decisionmaker facing this problem. Measures of performance (workload, timeliness and accuracy) are evaluated for each of these strategies. The results show that the strategy involving the use of the expert system improves significantly the accuracy of the organization, but requires more time and increases the workload of the decisionmaker using it.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1988
Accession Number
ADA196448

Entities

People

  • Alexander H. Levis
  • Didier M. Perdu

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Defense
  • Algorithms
  • Artificial Intelligence
  • Command And Control
  • Computations
  • Computer Science
  • Defense Systems
  • Expert Systems
  • Fuzzy Logic
  • Fuzzy Sets
  • Inference Engines
  • Information Exchange
  • Information Processing
  • Military Research
  • Organizational Structure
  • Petri Nets
  • Theoretical Computer Science

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Instructional Design and Training Evaluation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy
  • Fully Networked C3
  • Fully Networked C3 - Command and Control