Adaptive Problem Solving with Particle Systems

Abstract

Self-organizing particle systems ("swarms") consist of numerous autonomous, reflexive agents ("particles") whose collective movements through space are determined primarily by local influences. We are currently extending particle systems so that they not only move collectively, but also solve simple problems. This is done by giving the individual particles/agents a rudimentary intelligence in the form of a limited memory and a top-down, goal-directed control mechanism. Such enhanced particle systems are shown to be able to function effectively in performing simulated search-and-collect tasks. However, measuring the effects on particle collective performance of different design choices for individual agents proved to be difficult. We resolved this issue by allowing different agent teams to compete with one another under a variety of controlled conditions. This allowed us to demonstrate clearly how different agent features (independent vs. coordinated movement, exploratory vs. protective behaviors) impacted the behavior of the collective as a whole.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2004
Accession Number
ADA515825

Entities

People

  • A. Soto Rodríguez
  • James A. Reggia

Organizations

  • University of Maryland

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Availability
  • Classification
  • Computer Science
  • Computers
  • Contracts
  • Information Operations
  • Instructions
  • Intelligent Systems
  • Maryland
  • Monitoring
  • Particles
  • Security
  • Standards
  • Universities
  • Workshops

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Nanocomposite Materials Science
  • Systems Analysis and Design

Technology Areas

  • Space
  • Space - Spacecraft Maneuvers