XAgents: A Framework for Interpretable Rule-Based Multi-Agents Cooperation

• URL: http://arxiv.org/abs/2411.13932v1
• Date: Thu, 21 Nov 2024 08:28:27 GMT
• Title: XAgents: A Framework for Interpretable Rule-Based Multi-Agents Cooperation
• Authors: Hailong Yang, Mingxian Gu, Renhuo Zhao, Fuping Hu, Zhaohong Deng, Yitang Chen,
• Abstract summary: XAgents is an in-terpretable multi-agent cooperative framework based on the IF-THEN rule-based system. XAgetns transmits the task to the disparate domain rules, which subsequently generate the various responses. The incorporation of rule-based interpretability serves to bolster user confidence in the XAgents framework.
• Score: 4.872631281771186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting implicit knowledge and logical reasoning abilities from large language models (LLMs) has consistently been a significant challenge. The advancement of multi-agent systems has further en-hanced the capabilities of LLMs. Inspired by the structure of multi-polar neurons (MNs), we propose the XAgents framework, an in-terpretable multi-agent cooperative framework based on the IF-THEN rule-based system. The IF-Parts of the rules are responsible for logical reasoning and domain membership calculation, while the THEN-Parts are comprised of domain expert agents that generate domain-specific contents. Following the calculation of the member-ship, XAgetns transmits the task to the disparate domain rules, which subsequently generate the various responses. These re-sponses are analogous to the answers provided by different experts to the same question. The final response is reached at by eliminat-ing the hallucinations and erroneous knowledge of the LLM through membership computation and semantic adversarial genera-tion of the various domain rules. The incorporation of rule-based interpretability serves to bolster user confidence in the XAgents framework. We evaluate the efficacy of XAgents through a com-parative analysis with the latest AutoAgents, in which XAgents demonstrated superior performance across three distinct datasets. We perform post-hoc interpretable studies with SHAP algorithm and case studies, proving the interpretability of XAgent in terms of input-output feature correlation and rule-based semantics.

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