Cognitive Architectures for Embodied AI with Bayesian Inference

Abstract

This paper proposes a novel cognitive architecture for embodied artificial intelligence (AI) systems, designed to address the limitations of current approaches. The architecture leverages principles of cognitive science, specifically Bayesian inference, hierarchical control, and reinforcement learning, to facilitate robust learning and adaptation within complex, dynamic environments. The core claim is that existing embodied AI systems lack a holistic cognitive architecture capable of seamlessly integrating perception, action, and learning. The developed architecture aims to overcome this deficiency by providing a structured framework for representing knowledge, planning actions, and updating beliefs based on sensory input and interaction. Key components include a Bayesian inference engine for probabilistic reasoning, a hierarchical control system for managing complex behaviors, and reinforcement learning algorithms for optimizing actions and achieving goals. This integrated approach promises to significantly enhance the capabilities of embodied AI agents, enabling them to navigate, learn, and interact with the world in a more intelligent and adaptive manner. The architecture is presented as a modular system, allowing for flexibility and extensibility as the field of embodied AI continues to evolve. This document outlines the architectural design, the underlying principles, and the anticipated benefits of this approach.