Auto-GPT - Here we go again!
Sven Brueckner
Data Science & AI Expert: Advancing Self-Organizing Systems and Artificial Intelligence for Innovative Solutions
Last night I came across this post from langchain. Not to pick on this otherwise very good discussion, but... here we go again:
"What are “agents” and why are they important? For this discussion, we will use LangChain nomenclature, although it’s worth noting that this field is so new there’s no super standard terminology."
In the excitement about this new shiny gem (Auto-GPT etc.), some seem to forget that there is a LARGE body on Multi-Agent Systems research and engineering ready-to-go for these types of applications. So what, if the reasoning engine inside the autonomous agent happens to draw on large language models now? The rules of how to get these agents to produce useful, goal-directed outcomes are still the same!
I am watching my Auto-GPT agents create large circular arguments that span beyond their memory limit and never act - a classic phenomenon. I see the collective coming close to "the solution" only for random thoughts send them off into another round of exploration when they should be exploiting the information they've gathered already - that's nature's elegant balance in self-organizing systems.
Well, since OpenAI must have trained their models also on the #MAS literature, maybe the agents will re-discover how to organize. But wouldn't it be better if we just understand the field ourselves (with the help of chatGPT)?
or, in the words of GPT-4:
Multi-Agent Systems (MAS) theory is a field of study that focuses on understanding and designing systems composed of multiple autonomous agents that interact with each other to achieve specific goals. When applied to a goal-directed collective of autonomous chatGPT processes, MAS theory provides a framework for designing, implementing, and analyzing the collaboration and coordination among these chatGPT agents to achieve their objectives.
Here are some key aspects of applying MAS theory to a collective of autonomous chatGPT processes:
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Distributed problem-solving: MAS theory emphasizes the distribution of tasks and responsibilities among multiple agents, thereby allowing a collective of chatGPT processes to efficiently tackle complex problems. Each chatGPT agent can work on a different aspect of the problem, and by pooling their knowledge and resources, they can solve problems more efficiently than a single agent could.
Communication and coordination: Effective communication and coordination among chatGPT agents are crucial for achieving the collective goal. MAS theory provides insights into designing appropriate communication protocols and coordination mechanisms that enable agents to share information, negotiate, and make decisions collaboratively.
Agent heterogeneity: In a MAS, agents can have different roles, capabilities, and expertise. A collective of chatGPT processes can take advantage of this heterogeneity by having specialized agents that cater to specific tasks or domains, thereby improving the overall system performance and adaptability.
Learning and adaptation: As autonomous agents, chatGPT processes can learn and adapt over time to improve their performance. MAS theory offers methods and techniques for incorporating learning and adaptation mechanisms into the agents, allowing them to refine their strategies and knowledge based on their experiences and interactions with other agents.
Robustness and fault tolerance: A well-designed MAS can be more robust and fault-tolerant compared to a single-agent system. By distributing tasks and responsibilities among multiple chatGPT agents, the collective can continue to function even if some agents fail or become compromised.
Scalability: MAS theory provides a foundation for creating scalable systems. As the number of chatGPT agents increases, the system can continue to function effectively, and its performance may even improve due to the increased pool of resources and expertise.
In summary, applying Multi-Agent Systems theory to a goal-directed collective of autonomous chatGPT processes enables the design of more robust, efficient, and scalable systems. This approach leverages the combined strengths of multiple chatGPT agents, allowing them to collaboratively solve problems, share knowledge, and adapt to changing circumstances.
Data Science & AI Expert: Advancing Self-Organizing Systems and Artificial Intelligence for Innovative Solutions
1 年Of course, these groups of #agents that are talking to each other are experiencing #selforganization through the exchange of messages. But if they all draw on the same #vectordatabase for their memory, then we also have to consider the effects of #stigmergy. That's another hot topic in our research community #ACSOS.
great post. I've been engaging with a friend and colleague to address some use cases augmenting existing software with generative ai. Definitely interested in refined learning and insights for two use cases we are looking at.