This paper introduces Investigate-Consolidate-Exploit (ICE), a novel strategy for enhancing the adaptability and flexibility of AI agents through inter-task self-evolution. Unlike existing methods focused on intra-task learning, ICE promotes the transfer of knowledge between tasks for genuine self-evolution, similar to human experience learning. The strategy dynamically investigates planning and execution trajectories, consolidates them into simplified workflows and pipelines, and exploits them for improved task execution. Our experiments on the XAgent framework demonstrate ICE's effectiveness, reducing API calls by as much as 80% and significantly decreasing the demand for the model's capability. Specifically, when combined with GPT-3.5, ICE's performance matches that of raw GPT-4 across various agent tasks. We argue that this self-evolution approach represents a paradigm shift in agent design, contributing to a more robust AI community and ecosystem, and moving a step closer to full autonomy.

翻译：本文提出了一种名为调查-巩固-利用（ICE）的新策略，通过任务间自我进化来增强AI智能体的适应性和灵活性。与专注于任务内学习的现有方法不同，ICE促进了任务间知识的迁移，实现了真正的自我进化，类似于人类从经验中学习的过程。该策略动态地调查规划和执行轨迹，将其巩固为简化的工作流程和流水线，并利用这些流程来改进任务执行。我们在XAgent框架上的实验证明了ICE的有效性，将API调用减少了高达80%，并显著降低了对模型能力的需求。具体而言，当与GPT-3.5结合使用时，ICE在各种智能体任务中的性能与原始GPT-4相当。我们认为，这种自我进化方法代表了智能体设计的一种范式转变，有助于构建更强大的人工智能社区和生态系统，并向完全自主化迈出了重要一步。