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

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.