Appendix D: References
This section lists relevant resources and foundational concepts related to the T20 Multi-Agent System.
Multi-Agent Systems (MAS)
- Wooldridge, M. (2009). An Introduction to MultiAgent Systems. John Wiley & Sons.
- Russell, S. J., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Prentice Hall. (Chapters on intelligent agents and multi-agent systems)
Large Language Models (LLMs) & Google Gemini
- Google AI. (n.d.). Gemini API Documentation. Retrieved from [Google AI Developer Documentation Link]
- Google AI. (n.d.). Google Gemini Models Overview. Retrieved from [Google Gemini Overview Link]
- Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901. ( foundational paper on few-shot learning relevant to LLM prompting)
Python Libraries & Tools
- Python Software Foundation. (n.d.). Python Documentation. Retrieved from [python.org]
- Pydantic Documentation. (n.d.). Retrieved from [pydantic-docs.helpdocs.io]
- PyYAML Documentation. (n.d.). Retrieved from [pyyaml.org]
T20 Project Resources
- T20 Multi-Agent System Repository: [Link to GitHub Repository - e.g., https://github.com/your-username/t20-multi-agent]
- T20 Demo Logs: [Link to ./logs directory - e.g., ./logs]
Note: Replace bracketed links with actual URLs where applicable.