A Hybrid Cognitive-Reactive Multi-Agent Controller

Abstract

The purpose of this paper is to introduce a hybrid cognitive-reactive system, which integrates a machine- learning algorithm (SAMUEL, an evolutionary algorithm-based rule-learning system) with a computational cognitive model (written in ACT-R). In this system, the learning algorithm handles reactive aspects of the task and provides an adaptation mechanism, while the cognitive model handles cognitive aspects of the task and ensures the realism of the behavior. In this study, the controller architecture is used to implement a controller for a team of micro-air vehicles performing reconnaissance and surveillance.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2002
Accession Number
ADA482278

Entities

People

  • Alan C. Schultz
  • Farilee E. Mintz
  • J. Gregory Trafton
  • Magdalena D. Bugajska
  • Matthew Taylor

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Abstracts
  • Aircrafts
  • Algorithms
  • Evolutionary Algorithms
  • Information Operations
  • Learning
  • Machine Learning
  • Micro Air Vehicles
  • Military Research
  • Reconnaissance
  • Surveillance
  • Switzerland

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems