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

Adaptability and diversity in simulated turn-taking behavior.

Hiroyuki Iizuka1, Takashi Ikegami

  • 1Department of General Systems Sciences, The Graduate School of Arts and Sciences, University of Tokyo, 3-8-1 Komaba, Tokyo 153-8902, Japan. ezca@sacral.c.u-tokyo.ac.jp

Artificial Life
|October 14, 2004
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

Community First Theory: How Collective Organization Generates Individual Diversity.

Entropy (Basel, Switzerland)·2026
Same author

Exploring Cultural Evolution Through Modular Dynamics in Temporal Hashtag Networks.

Entropy (Basel, Switzerland)·2026
Same author

Dwell Time Outperforms Social and Chemical Predictors of Behavioural Transitions in Ants.

Entropy (Basel, Switzerland)·2026
Same author

From text to motion: grounding GPT-4 in a humanoid robot "Alter3".

Frontiers in robotics and AI·2025
Same author

Editorial Introduction to the 2023 Conference on Artificial Life Special Issue.

Artificial life·2025
Same author

Spontaneous Emergence of Agent Individuality Through Social Interactions in Large Language Model-Based Communities.

Entropy (Basel, Switzerland)·2025

This study simulates turn-taking behavior in coupled agents using recurrent neural networks. Chaotic agents adapt better, while regular agents show robustness, revealing a trade-off between these traits.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Complex Systems

Background:

  • Turn-taking is a fundamental interaction in social systems.
  • Simulating emergent behavior in multi-agent systems is crucial for understanding complex interactions.

Purpose of the Study:

  • To simulate and analyze turn-taking behavior in a coupled-agents system.
  • To investigate the impact of regular versus chaotic agent dynamics on interaction robustness and adaptability.

Main Methods:

  • Modeling agents as mobile robots with recurrent neural networks for motor control and internal dynamics.
  • Evolving network structures to establish turn-taking in a 2D arena.
  • Analyzing agent behavior under regular and chaotic dynamics.

Related Experiment Videos

Main Results:

  • Chaotic agents exhibit higher sensitivity to external inputs.
  • Regular agents demonstrate greater robustness against noisy inputs due to restricted dynamics.
  • A complementary relationship between robustness and adaptability was observed.
  • Recoupling agents from different generations led to novel turn-taking behaviors.

Conclusions:

  • Agent dynamics (regular vs. chaotic) significantly influence interaction properties like robustness and adaptability.
  • Chaotic agents show potential for synthesizing new forms of interaction.
  • The study highlights a fundamental trade-off between robustness and adaptability in coupled agent systems.