Cooperative Area Extension of PSO Transfer Learning vs. Uncertainty in a simulated Swarm Robotics

Adham Atyabi, David Powers

    Research output: Contribution to conferencePaper

    5 Citations (Scopus)

    Abstract

    The study investigates the effectiveness of 2 variations of Particle Swarm Optimization (PSO) called Area Extended PSO (AEPSO) and Cooperative AEPSO (CAEPSO) in simulated robotic environments affected by a combinatorial noise. Knowledge Transfer, the use of the expertise and knowledge gained from previous experiments, can improve the robots decision making and reduce the number of wrong decisions in such uncertain environments. This study investigates the impact of transfer learning on robots' performance in such hostile environment. The results highlight the feasibility of CAEPSO to be used as the controller and decision maker of a swarm of robots in the simulated uncertain environment when gained expertise from past training is transferred to the robots in the testing phase.

    Original languageEnglish
    Pages177-184
    Number of pages8
    Publication statusPublished - 3 Dec 2013
    EventICINCO 2013 -
    Duration: 29 Jul 2013 → …

    Conference

    ConferenceICINCO 2013
    Period29/07/13 → …

    Keywords

    • Cooperative Learning
    • Knowledge Transfer
    • Particle Swarm Optimization
    • Swarm Robotics
    • Transfer Learning

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