Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines
This work addresses energy efficiency in manufacturing systems, but it appears incremental as it builds on existing active inference methods with specific architectural tweaks.
The paper tackled energy-efficient control of parallel and identical machines in manufacturing by applying deep active inference with tailored enhancements like multi-step transition and hybrid horizon methods, resulting in effective improvements as demonstrated experimentally.
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 challenges posed by the problem's stochastic nature and delayed policy response by introducing tailored enhancements to existing agent architectures. Specifically, we introduce multi-step transition and hybrid horizon methods to mitigate the need for complex planning. Our experimental results demonstrate the effectiveness of these enhancements and highlight the potential of the active inference-based approach.