Cognitive Models for Learning to Control Dynamic Systems

Abstract

Report developed under STTR contract for topic "Cognitive models for learning to control dynamic systems" demonstrated a swarm intelligence learning algorithm and its application in unmanned aerial vehicle (UAV) mission planning. A new UAV assignment model was developed that reduces the dimension of the solution space and is easily adapted by computational intelligence algorithms. A version of particle swarm optimization (PSO) was applied to accomplish the mission optimization. Numerical experimental results illustrate that it efficiently achieves the optima and demonstrates the effectiveness of combining the model and PSO to solve complex UAV assignment problems. The time to complete mission plans for operationally realistic scenarios is reduced by 3-4 orders of magnitude compared with the mixed-integer linear programming approach being used by AFRL at WPAFB. A computer game was also developed to investigate how humans interact with swarm intelligence. The game is based on an NK landscape. It is concluded that the combination of a human-swarm team may have advantages in certain environments, such as dynamic decision making tasks.

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Document Details

Document Type
Technical Report
Publication Date
May 30, 2008
Accession Number
ADA482256

Entities

People

  • Russ Eberhart
  • Xiaohui Hu
  • Yaobin Chen

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • C4I

DTIC Thesaurus Topics

  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Computational Complexity
  • Computational Science
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Information Processing
  • Integer Programming
  • Machine Learning
  • Multiobjective Optimization
  • Optimization
  • Particle Swarm Optimization
  • Self Organizing Systems
  • Swarm Intelligence
  • Systems Engineering
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Operations Research
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control
  • Space
  • Space - Spacecraft Maneuvers