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【第155期】IntellAgent：多智能体框架
Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI SystemsSummary
This document introduces IntellAgent, a novel, open-source multi-agent framework designed to evaluate conversational AI systems. IntellAgent addresses the shortcomings of traditional methods by automating the creation of diverse, realistic scenarios using policy-driven graph modeling, event generation, and user-agent simulations. The framework leverages a policy graph to represent policy relationships and complexities, enabling detailed diagnostics of agent performance. Unlike existing benchmarks, IntellAgent offers fine-grained insights into policy adherence and identifies specific areas for improvement. Experiments show that IntellAgent provides a robust alternative for evaluating conversational agents and correlating with existing benchmarks, despite relying on synthetic data. The system is implemented using Langgraph and provides a means to assess different large language models in complex chatbot environments.
本文件介绍了 IntellAgent，一个新颖的开源多智能体框架，旨在评估对话式人工智能系统。IntellAgent 通过策略驱动的图建模、事件生成和用户代理模拟，自动创建多样化且逼真的场景，从而弥补了传统方法的不足。该框架利用策略图来表示策略关系及其复杂性，使得对智能体的性能进行详细诊断成为可能。与现有基准测试不同，IntellAgent 能够提供细粒度的洞察，评估策略遵循情况并识别具体的改进点。实验表明，尽管依赖于合成数据，IntellAgent 依然能够作为评估对话代理的有力替代方案，并与现有基准测试结果呈现相关性。该系统基于 Langgraph 实现，并可用于评估不同的大型语言模型在复杂聊天机器人环境中的表现。
原文链接：https://arxiv.org/abs/2501.11067
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By 任雨山Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。
今天的主题是：IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI SystemsSummary
This document introduces IntellAgent, a novel, open-source multi-agent framework designed to evaluate conversational AI systems. IntellAgent addresses the shortcomings of traditional methods by automating the creation of diverse, realistic scenarios using policy-driven graph modeling, event generation, and user-agent simulations. The framework leverages a policy graph to represent policy relationships and complexities, enabling detailed diagnostics of agent performance. Unlike existing benchmarks, IntellAgent offers fine-grained insights into policy adherence and identifies specific areas for improvement. Experiments show that IntellAgent provides a robust alternative for evaluating conversational agents and correlating with existing benchmarks, despite relying on synthetic data. The system is implemented using Langgraph and provides a means to assess different large language models in complex chatbot environments.
本文件介绍了 IntellAgent，一个新颖的开源多智能体框架，旨在评估对话式人工智能系统。IntellAgent 通过策略驱动的图建模、事件生成和用户代理模拟，自动创建多样化且逼真的场景，从而弥补了传统方法的不足。该框架利用策略图来表示策略关系及其复杂性，使得对智能体的性能进行详细诊断成为可能。与现有基准测试不同，IntellAgent 能够提供细粒度的洞察，评估策略遵循情况并识别具体的改进点。实验表明，尽管依赖于合成数据，IntellAgent 依然能够作为评估对话代理的有力替代方案，并与现有基准测试结果呈现相关性。该系统基于 Langgraph 实现，并可用于评估不同的大型语言模型在复杂聊天机器人环境中的表现。
原文链接：https://arxiv.org/abs/2501.11067