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The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Learning multi-agent cooperation.

Corban Rivera¹, Edward Staley¹, Ashley Llorens²

¹Johns Hopkins Applied Physics Lab, Intelligent Systems Center, Laurel, MD, United States.

Frontiers in Neurorobotics

|October 31, 2022

Summary

This study introduces AI Arena, a scalable framework for artificial intelligence (AI) research, enabling complex multi-agent learning in challenging environments. The framework facilitates cooperation among distributed AI agents, advancing AI applications in urban and defense settings.

Keywords:

artificial intelligence learned cooperation multi-agent policy learning reinforcement learning

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Area of Science:

Artificial Intelligence
Machine Learning
Multi-Agent Systems

Background:

Reinforcement learning (RL) has driven significant AI breakthroughs across various domains.
Existing AI frameworks often lack support for complex, distributed, heterogeneous multi-agent systems.
This gap limits AI research in critical applications like dense urban environments and defense scenarios.

Purpose of the Study:

To introduce the AI Arena, a novel, scalable framework designed for AI research in complex operating environments.
To provide flexible abstractions for associating agents, policies, and learning algorithms.
To facilitate research on multi-agent learning, cooperation, and curriculum design for advanced AI applications.

Main Methods:

Development of the AI Arena: a scalable framework with flexible abstractions.
Association of agents with policies and policies with learning algorithms within the framework.
Evaluation of the framework's strengths, curriculum design impact, and multi-agent learning paradigms.

Main Results:

Demonstrated the strengths of the AI Arena framework in supporting complex AI research.
Highlighted the critical role of curriculum design in effective AI agent training.
Quantified the impact of multi-agent learning on the emergence of cooperative behaviors.

Conclusions:

The AI Arena provides a robust platform for advancing AI research in distributed, heterogeneous multi-agent systems.
Effective curriculum design is essential for optimizing learning in complex AI environments.
The framework supports the study of cooperation and advanced AI capabilities for real-world applications.