AriGraph: Learning Knowledge Graph World Models with Episodic Memory for
  LLM Agents
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.
668b8e236ce40bc8352c5169
Petr Anokhin
claimed_verified
2024-07-08T20:00:47.320Z
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/avatars/a50c7e1d22174f06fbaf99e8caca7c37.svg
Petr Anokhin
user
petranokhin
668b8e236ce40bc8352c516a
Nikita Semenov
668b8e236ce40bc8352c516b
Artyom Sorokin
admin_assigned
2024-07-08T21:07:01.127Z
64beb75986e7fb5b8a8fa731
/avatars/7053c64142300fa446ba5870eb28c3ee.svg
Artyom Sorokin
user
griver
668b8e236ce40bc8352c516c
Dmitry Evseev
admin_assigned
2024-07-08T21:07:08.303Z
6650722c0d018ba9fb43aff1
/avatars/eac71d36bc1fedc6f5d2266426d65ad1.svg
Dmitry Evseev
user
dmitry-evseev-nd
668b8e236ce40bc8352c516d
Mikhail Burtsev
admin_assigned
2024-07-08T21:07:13.843Z
639c6e978a34ed9a404c6a7b
/avatars/c98ca8c9f9ed8509c2f1bb6aa994fd57.svg
MIKHAIL BURTSEV
user
mbur
668b8e236ce40bc8352c516e
Evgeny Burnaev
AriGraph: Learning Knowledge Graph World Models with Episodic Memory for
  LLM Agents
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.
AriGraph, a memory graph integrating semantic and episodic memories, enhances the exploratory and planning capabilities of an LLM-based agent, outperforming existing methods in complex zero-shot tasks.
Large Language Models (LLMs) autonomous agents memory graph semantic memories episodic memories associative retrieval TextWorld planning decision-making full-history summarization Retrieval-Augmented Generation