Distributed coordination of heterogeneous agents using a semantic overlay network and a goal-directed graphplan...

Area of Science:

• Artificial Intelligence
• Multi-Agent Systems
• Distributed Computing

Background:

• Coordinating multiple autonomous agents in complex problem-solving scenarios presents significant challenges.
• Existing systems often struggle with efficient skill discovery and task delegation in large, unstructured networks.

Purpose of the Study:

• To develop a distributed coordination system for seamless agent cooperation in problem-solving.
• To enable efficient skill localization and task delegation within large agent societies.

Main Methods:

• A dynamically built semantic overlay network for skill discovery.
• A distributed, goal-directed Graphplan algorithm for partial problem-solving.
• Heuristics for distributing backward-search processes among idle agents.

Main Results:

• The system allows agents to contribute partially and delegate subproblems effectively.
• The semantic overlay network facilitates efficient skill location in large, unstructured environments.
• The goal-directed Graphplan algorithm mitigates the 'distraction' problem common in forward-chaining algorithms.

Conclusions:

• The proposed approach creates a scalable and efficient agent society for complex, distributable problems.
• The coordination system enhances problem-solving capabilities through intelligent task delegation and skill sharing.