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：本文探讨了人工智能两个子领域——大型语言模型（LLMs）与认知架构（CAs）——在开发具有智能行为的人工智能体中的融合。我们提出了三种融合方法，每种方法均基于理论模型并得到初步实证证据支持。模块化方法引入四种集成程度不同的模型，采用思维链提示技术，借鉴增强型LLM、认知通用模型及认知模拟理论；主体方法受心智社会理论与LIDA认知架构启发，提出由LLM或符号组件驱动、在微观与宏观认知层面交互的主体集合；神经符号方法则借鉴CLARION认知架构，提出自底向上学习从LLM层提取符号表征、自顶向下引导利用符号表征指导LLM层提示工程的双向模型。这些方法旨在发挥LLM与CA各自优势、规避其弱点，从而推动更稳健人工智能系统的发展。我们探讨了每种方法面临的权衡与挑战。