Modeling Eye Movement Sequences Using Conceptual Clustering Techniques

Abstract

An algorithm for clustering noisy continuous numeric data was developed in a learning system called 2DCG (two dimensional cluster generalization). The 2DCG system operates in a two-dimensional space, but a general system could operate in an N-dimensional space. The objective of the system was to learn a set of rules which modeled human observers (in the application presented here, this model predicted changes in the eye position of human observers during a visual monitoring task). The rule set had to be complete, consistent, and nonredundant, while minimizing the number and maximizing the generality of the rules. The development of this model and its performance in accounting for noisy data are described. Keywords: Artificial intelligence, Concept learning, Conceptual clustering, Cognitive modeling, Eye movements, Grouping, Rule-based systems, Segmentation, Visual monitoring.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1988
Accession Number
ADA199403

Entities

People

  • Don R. Lyon
  • Michael S. Belofsky

Organizations

  • University of Dayton

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Aircrafts
  • Artificial Intelligence
  • Biological Sciences
  • Classification
  • Data Sets
  • Eye Movements
  • Human Resources
  • Identification
  • Image Processing
  • Security
  • Standards
  • Training
  • Two Dimensional
  • United States
  • Universities

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

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