Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines

arXiv:2406.09322v1 Announce Type: new
Abstract: We investigate the application of active inference in developing energy-efficient control agents for manufacturing systems. Active inference, rooted in neuroscience, provides a unified probabilistic framework integrating perception, learning, and action, with inherent uncertainty quantification elements. Our study explores deep active inference, an emerging field that combines deep learning with the active inference decision-making framework. Leveraging a deep active inference agent, we focus on controlling parallel and identical machine workstations to enhance energy efficiency. We address …

abstract action agents application arxiv control cs.ai cs.lg elements energy framework inference machines manufacturing neuroscience perception quantification study systems type uncertainty