Debate-based Cooperation
Summary: An agent receives feedback from other agents, and adjusts the thoughts and behaviours during the debate with other agents until a consensus is reached.
Context: A compound AI system can integrate multiple agents to provide more comprehensive services. The included agents need to collaborate on the same task while having their own perspectives.
Problem: How to leverage multiple agents to create refined responses, while facilitating the evolution of agents.
Forces:
- Diversity. Different agents may have various opinions to help refine the generated responses to users.
- Lack of adaptability. An agent may exhibit limited creativity in reasoning and response generation when given new context or tasks.
- Lack of explainability. The interaction process of agents should be interpreted for auditing if violations are detected.
Solution: Fig. 1 depicts a high-level graphical representation of debate-based cooperation. A user can send queries to an agent, which will then share the questions with other agents. Given the shared question, each agent generates its own initial responses, and subsequently, a round of debate will start between the agents. Agents will propagate their initial response in a decentralised manner to each other for verification, while also providing instructions and potential planning directions to construct a more comprehensive response based on inclusive and collective outcomes. In addition, agents may utilise a shared memory in certain circumstances, or allow each other to access the respective memory facilitating the debate. This debate process can be iterative to enhance the performance of all participating agents. Debate-based cooperation can end according to a predefined number of debate rounds, or the agents will continue the procedure until a consensus answer is obtained.
![](https://i0.wp.com/research.csiro.au/ss/wp-content/uploads/sites/281/2024/07/debate.jpg?resize=459%2C290&ssl=1)
Figure 1. Debate-based cooperation.
Benefits:
- Adaptability. Agents can adapt to other agents during the debate procedure, achieving continuous learning and evolution.
- Explainability. Debate-based cooperation is structured with agents’ arguments and presented evidence, preserving transparency and explainability of the whole procedure.
- Critical thinking. Arguing with other agents can help an agent develop the ability of critical thinking for future reasoning process.
Drawbacks:
- Limited capability. The effectiveness of debate-based cooperation relies on agents’ capabilities of reasoning, argument, and evaluation of other agents’ statement.
- Data privacy. Agents may need to withhold certain sensitive information, which can affect the debate process.
- Overhead. The complexity of debate may increase the communication and computation overhead.
- Scalability preservation. The system scalability may be affected as the number of participating agents increases. The coordination of agents and processing of their arguments may become complex.
Known uses:
- crewAI. crewAI provides a multi-agent orchestration framework where multiple agents can be grouped for discussion on a given topic.
- Liang et al. [1] leverage multi-agent debate to address the issue of “Degeneration-of-Thought”. Within the debate, an agent needs to persuade another and correct the mistakes.
- Du et al. [2] employ multiple agents to discuss the given user input, and the experiment results indicate that the agents can converge on a consensus answer after multiple rounds.
- Chen et al. [3] explore the negotiation process in a multi-agent system, where each agent can perceive the outcomes of other agents, and adjust its own strategies.
- Li et al. [4] propose a framework including peer rank and discussion between agents, to mitigate the biases in automated evaluation process.
Related patterns:
- Cross-reflection. Agents can decide the reflective feedback to another agent via debate-based cooperation.
- Voting-based and role-based cooperation. Debate-based cooperation can be regarded as an alternative of other cooperation patterns by hosting a debate between agents, whilst they can be applied together to complement each other.
- Tool/agent registry. Agents participating in the debate process can be employed via tool/agent registry.
References:
[1] T. Liang, Z. He, W. Jiao, X.Wang, Y.Wang, R.Wang, Y. Yang, Z. Tu, and S. Shi, “Encouraging divergent thinking in large language models through multi-agent debate,” arXiv preprint arXiv:2305.19118, 2023.
[2] Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch, “Improving factuality and reasoning in language models through multiagent debate,” arXiv preprint arXiv:2305.14325, 2023.
[3] H. Chen, W. Ji, L. Xu, and S. Zhao, “Multi-agent consensus seeking via large language models,” arXiv preprint arXiv:2310.20151, 2023.
[4] R. Li, T. Patel, and X. Du, “PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations,” arXiv preprint arXiv:2307.02762, 2023.