Principles of MAS
A multi-agent system comprises multiple interacting intelligent entities. The research shift is from single monolithic models to ecosystems of specialized agents that must coordinate to achieve complex objectives.
Coordination and Communication
- Consensus Protocols: Implementation of algorithms (e.g., Paxos, Raft) to ensure agents maintain a synchronized state of information.
- Agent Communication Languages (ACL): The use of structured protocols, including CNL-based languages, for the exchange of intentions and evidence.
- Economic Coordination: Utilizing bidding mechanisms and auction theory for task allocation and resource management among agents.
Legacy and Theoretical Foundations
- NetLogo: A multi-agent programmable modeling environment used extensively for simulating complex social and natural phenomena.
- FIPA Standards: The Foundation for Intelligent Physical Agents defined the first international standards for agent interoperability and communication.
- Swarm Intelligence: Research into decentralized, self-organized systems (e.g., Ant Colony Optimization) that provide models for simple agent coordination.
- BDI (Belief-Desire-Intention) Model: A classic software model for developing intelligent agents, focusing on the internal state transitions of goal-oriented entities.
Analysis
Multi-agent architectures enable distributed verification processes. By implementing roles such as generator and adversarial verifier, systems can achieve Epistemic Redundancy, where multiple independent models validate outputs to surface errors or biases that a single model would fail to detect.
References
- AutoGen (Microsoft GitHub): A framework for coordinating conversing agents based on LLMs.
- GPT Researcher (GitHub): An implementation of autonomous research agents.