title: AriGraph: Learning Knowledge Graph World Models with Episodic Memory for
    LLM Agents
summary: 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.
paper_authors._id: 668b8e236ce40bc8352c5169
paper_authors.name: Petr Anokhin
paper_authors.status: claimed_verified
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paper_authors.fullname: Petr Anokhin
paper_authors.type: user
paper_authors.user: petranokhin
paper_authors._id: 668b8e236ce40bc8352c516a
paper_authors.name: Nikita Semenov
paper_authors._id: 668b8e236ce40bc8352c516b
paper_authors.name: Artyom Sorokin
paper_authors.status: admin_assigned
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paper_authors._id: 64beb75986e7fb5b8a8fa731
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paper_authors.fullname: Artyom Sorokin
paper_authors.type: user
paper_authors.user: griver
paper_authors._id: 668b8e236ce40bc8352c516c
paper_authors.name: Dmitry Evseev
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-08T21:07:08.303Z
paper_authors._id: 6650722c0d018ba9fb43aff1
paper_authors.avatarUrl: /avatars/eac71d36bc1fedc6f5d2266426d65ad1.svg
paper_authors.fullname: Dmitry Evseev
paper_authors.type: user
paper_authors.user: dmitry-evseev-nd
paper_authors._id: 668b8e236ce40bc8352c516d
paper_authors.name: Mikhail Burtsev
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-08T21:07:13.843Z
paper_authors._id: 639c6e978a34ed9a404c6a7b
paper_authors.avatarUrl: /avatars/c98ca8c9f9ed8509c2f1bb6aa994fd57.svg
paper_authors.fullname: MIKHAIL BURTSEV
paper_authors.type: user
paper_authors.user: mbur
paper_authors._id: 668b8e236ce40bc8352c516e
paper_authors.name: Evgeny Burnaev
paper_title: AriGraph: Learning Knowledge Graph World Models with Episodic Memory for
    LLM Agents
paper_summary: 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.
paper_ai_keywords: Large Language Models (LLMs) autonomous Agents unstructured memory representations hallucination planning memory graph semantic memories episodic memories associative retrieval environmental model exploratory capabilities decision-making zero-shot basis TextWorld environment full-history summarization Retrieval-Augmented Generation cooking challenge TextWorld Problems competition house cleaning puzzle Hunting