Grounding Agent Reasoning in Image Schemas: A Neurosymbolic Approach to Embodied Cognition
François Olivier, Zied Bouraoui
arXiv Preprint Archive March 31, 2025 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Keywords | Cs.ai Cs.cl |
| Key points | Proposes a neurosymbolic framework in which customized LLMs translate natural language into formal representations of image schemas, the recurring sensorimotor patterns said to structure human cognition. Argues that grounding agent understanding in these conceptual structures would enhance efficiency and interpretability and support more intuitive human-agent interaction through shared embodied understanding. |
Abstract
Despite advances in embodied AI, agent reasoning systems still struggle to capture the fundamental conceptual structures that humans naturally use to understand and interact with their environment. To address this, we propose a novel framework that bridges embodied cognition theory and agent systems by leveraging a formal characterization of image schemas, which are defined as recurring patterns of sensorimotor experience that structure human cognition. By customizing LLMs to translate natural language descriptions into formal representations based on these sensorimotor patterns, we will be able to create a neurosymbolic system that grounds the agent's understanding in fundamental conceptual structures. We argue that such an approach enhances both efficiency and interpretability while enabling more intuitive human-agent interactions through shared embodied understanding.