This paper explores the integration of two AI subdisciplines employed in the development of artificial agents that exhibit intelligent behavior: Large Language Models (LLMs) and Cognitive Architectures (CAs). We present three integration approaches, each grounded in theoretical models and supported by preliminary empirical evidence. The modular approach, which introduces four models with varying degrees of integration, makes use of chain-of-thought prompting, and draws inspiration from augmented LLMs, the Common Model of Cognition, and the simulation theory of cognition. The agency approach, motivated by the Society of Mind theory and the LIDA cognitive architecture, proposes the formation of agent collections that interact at micro and macro cognitive levels, driven by either LLMs or symbolic components. The neuro-symbolic approach, which takes inspiration from the CLARION cognitive architecture, proposes a model where bottom-up learning extracts symbolic representations from an LLM layer and top-down guidance utilizes symbolic representations to direct prompt engineering in the LLM layer. These approaches aim to harness the strengths of both LLMs and CAs, while mitigating their weaknesses, thereby advancing the development of more robust AI systems. We discuss the tradeoffs and challenges associated with each approach.

翻译：本文探讨了用于开发展现智能行为的人工智能体的两个子领域——大型语言模型（LLMs）与认知架构（CAs）的整合。我们提出了三种整合方法，每种方法均以理论模型为基础，并得到初步实证证据的支持。模块化方法引入了四种具有不同整合程度的模型，利用思维链提示技术，并从增强型LLMs、通用认知模型以及模拟认知理论中汲取灵感。代理方法受心智社会理论和LIDA认知架构启发，提出构建由LLMs或符号组件驱动、在微观与宏观认知层面相互交互的代理集合。神经符号方法受CLARION认知架构启发，提出一种模型：自底向上的学习从LLM层提取符号表征，而自顶向下的引导则利用符号表征来指导LLM层中的提示工程。这些方法旨在发挥LLMs与CAs的双重优势，同时弥补其弱点，从而推动更稳健人工智能系统的发展。我们讨论了每种方法相关的权衡与挑战。