A popperian neuro-symbolic agent that learns through LLM-based conjecture and refutation

Recent advancements in Large Language Models (LLMs) have enabled new manners of integration with decision systems and autonomous agents, although they offer no guarantees for deductive reasoning nor plan generation. However, their capacity to provide probable, plausible answers to given inputs makes them suitable to be used as abductive reasoners, and brings the field to the era of popperian agents that weed out invalid possibilities in order to quicken learning. In this paper, we present a popperian agent architecture that uses an LLM-based component for epistemic queries in order to learn the dynamics of its environment: the LLM conjectures hypotheses, which are validated through thorough experimentation, and ultimately form a planning model that can be reused at near-zero cost, without the intervention of the LLM. We also present a provenance ontology that allows connecting each learnt piece of information with the experiments that validated it and the phenomenon that caused the piece of information to be conjectured at all.