Symbolic reasoning systems have been used in cognitive architectures to provide inference and planning capabilities. However, defining domains and problems has proven difficult and prone to errors. Moreover, Large Language Models (LLMs) have emerged as tools to process natural language for different tasks. In this paper, we propose the use of LLMs to tackle these problems. This way, this paper proposes the integration of LLMs in the ROS 2-integrated cognitive architecture MERLIN2 for autonomous robots. Specifically, we present the design, development and deployment of how to leverage the reasoning capabilities of LLMs inside the deliberative processes of MERLIN2. As a result, the deliberative system is updated from a PDDL-based planner system to a natural language planning system. This proposal is evaluated quantitatively and qualitatively, measuring the impact of incorporating the LLMs in the cognitive architecture. Results show that a classical approach achieves better performance but the proposed solution provides an enhanced interaction through natural language.

翻译：符号推理系统已被用于认知架构中，以提供推理和规划能力。然而，定义领域和问题被证明是困难且容易出错的。此外，大型语言模型（LLMs）已成为处理不同任务自然语言的工具。在本文中，我们提议使用LLMs来解决这些问题。因此，本文提出了将LLMs集成到用于自主机器人的ROS 2集成认知架构MERLIN2中。具体而言，我们介绍了如何在MERLIN2的深思熟虑过程中利用LLMs推理能力的设计、开发与部署。结果，深思熟虑系统从基于PDDL的规划器系统更新为自然语言规划系统。该提案通过定量和定性评估，衡量了将LLMs纳入认知架构的影响。结果表明，经典方法实现了更好的性能，但所提出的解决方案通过自然语言提供了增强的交互能力。