  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.
