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A framework for establishing shared, task-oriented understanding in hybrid open multi-agent systems.

Nikolaos Kondylidis1, Ilaria Tiddi1, Annette Ten Teije1

  • 1Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.

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|May 1, 2025
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Summary
This summary is machine-generated.

Agents in open multi-agent systems (OMAS) must learn to communicate, especially in hybrid human-AI settings. This study offers a framework to guide designers in creating agents that establish shared understanding with minimal assumptions and interactions.

Keywords:
human-agent collaborationhuman-agent communicationhybrid open multi-agent systemsshared understandingtask-oriented understanding establishment

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Area of Science:

  • Artificial Intelligence
  • Multi-Agent Systems
  • Human-Computer Interaction

Background:

  • Open Multi-Agent Systems (OMAS) require agents to learn communication protocols dynamically.
  • Hybrid settings with human and artificial agents present unique challenges for inter-agent communication.
  • Minimizing a priori assumptions and human interaction is crucial for effective learning in OMAS.

Purpose of the Study:

  • To provide a framework for analyzing the process of establishing shared task-oriented understanding in OMAS.
  • To specifically address the challenges in hybrid populations involving human and artificial agents.
  • To guide researchers in designing agents capable of interacting with humans in unforeseen scenarios.

Main Methods:

  • A fine-grained analysis of shared understanding establishment in OMAS.
  • Development of a framework detailing design decisions for agent interaction.
  • Examination of how human inclusion impacts these design components.

Main Results:

  • The framework offers a uniform method for analyzing diverse existing approaches to shared understanding.
  • Existing methods show limitations when applied to hybrid agent populations.
  • The study identifies how to resolve these limitations for hybrid OMAS.

Conclusions:

  • The proposed framework aids in designing agents for effective human-AI collaboration in OMAS.
  • It highlights the need for adaptable communication strategies in hybrid systems.
  • The research facilitates the development of more robust and adaptable intelligent agents.