AriGraph：利用情节记忆学习知识图世界模型，用于LLM智能体

AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents

July 5, 2024

作者: Petr Anokhin, Nikita Semenov, Artyom Sorokin, Dmitry Evseev, Mikhail Burtsev, Evgeny Burnaev

cs.AI

摘要

生成式人工智能的进展拓宽了大型语言模型（LLMs）在自主代理开发中的潜在应用。实现真正的自主性需要积累和更新通过与环境互动获得的知识，并有效利用它。当前基于LLM的方法利用过去的经验，使用完整的观察历史、摘要或检索增强。然而，这些非结构化的记忆表示并不促进复杂决策所必需的推理和规划。在我们的研究中，我们引入了AriGraph，一种新颖的方法，其中代理构建一个记忆图，整合语义和情节记忆，同时探索环境。这种图结构促进了互相关联概念的高效联想检索，与代理当前状态和目标相关，从而作为一个有效的环境模型，增强了代理的探索和规划能力。我们证明，我们的Ariadne LLM代理，配备了这种提议的记忆架构，增强了规划和决策能力，在TextWorld环境中能够有效地处理零样本基础上的复杂任务。我们的方法在各种任务中明显优于已建立的方法，如完整历史、摘要和检索增强生成，包括第一届TextWorld Problems比赛中的烹饪挑战以及新颖任务，如清洁房屋和解谜寻宝。

English

Advancements in generative AI have broadened the potential applications of Large Language Models (LLMs) in the development of autonomous agents. Achieving true autonomy requires accumulating and updating knowledge gained from interactions with the environment and effectively utilizing it. Current LLM-based approaches leverage past experiences using a full history of observations, summarization or retrieval augmentation. However, these unstructured memory representations do not facilitate the reasoning and planning essential for complex decision-making. In our study, we introduce AriGraph, a novel method wherein the agent constructs a memory graph that integrates semantic and episodic memories while exploring the environment. This graph structure facilitates efficient associative retrieval of interconnected concepts, relevant to the agent's current state and goals, thus serving as an effective environmental model that enhances the agent's exploratory and planning capabilities. We demonstrate that our Ariadne LLM agent, equipped with this proposed memory architecture augmented with planning and decision-making, effectively handles complex tasks on a zero-shot basis in the TextWorld environment. Our approach markedly outperforms established methods such as full-history, summarization, and Retrieval-Augmented Generation in various tasks, including the cooking challenge from the First TextWorld Problems competition and novel tasks like house cleaning and puzzle Treasure Hunting.