Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Talking helps: evolving communicating agents for the predator-prey pursuit problem.

K C Jim1, C L Giles

  • 1NEC Research Institute, Inc., 4 Independence Way, Princeton, NJ 08540, USA. kamjim@research.nj.nec.com

Artificial Life
|February 27, 2001
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Poster presentations at medical conferences: an effective way of disseminating research?

Clinical medicine (London, England)·2011
Same author

Optical computing: introduction by the feature editors.

Applied optics·2010
Same author

Learning, invariance, and generalization in high-order neural networks.

Applied optics·2010
Same author

Multiplexed coherent optical processor for calculating generalized moments.

Optics letters·2009
Same author

Neural networks and hybrid intelligent models: foundations, theory, and applications.

IEEE transactions on neural networks·2008
Same author

Attractive periodic sets in discrete-time recurrent networks (with emphasis on fixed-point stability and bifurcations in two-neuron networks).

Neural computation·2001

Evolving communication languages for predator agents significantly enhances their performance in predator-prey scenarios. Larger language sizes lead to better outcomes, and an incremental approach speeds up the evolution process.

Area of Science:

  • Artificial Intelligence
  • Computational Biology
  • Evolutionary Computation

Background:

  • Multi-agent systems often require effective communication strategies for optimal performance.
  • Simultaneous communication models and predator-prey pursuit problems are key areas in AI research.

Purpose of the Study:

  • To evolve multi-agent communication languages for predator agents using a genetic algorithm.
  • To analyze the impact of language size on predator performance and system behavior.
  • To develop an efficient method for evolving complex communication systems.

Main Methods:

  • Utilized a genetic algorithm to evolve predator languages in a predator-prey pursuit simulation.
  • Modeled the communicating multi-agent system as a Mealy finite state machine.

Related Experiment Videos

  • Introduced an incremental language size increase for efficient evolution.

    • Main Results:

    • Evolved communication languages significantly improved predator performance.
    • Increasing language size further enhanced predator effectiveness and system capabilities.
    • The incremental approach reduced evolution time and resulted in smaller Mealy machines.
    • Evolved predators outperformed previous state-of-the-art in similar prey scenarios.

    Conclusions:

    • Communication is crucial for enhancing multi-agent system performance in complex tasks.
    • Language size is a critical factor influencing emergent behavior and task success.
    • An incremental evolution strategy offers an efficient pathway to developing sophisticated agent communication.