Active Inference

Active inference is a mathematical framework modeling how agents, biological or artificial, make decisions and learn by minimizing prediction errors ("surprise") within their environment. Current research focuses on applying active inference to diverse problems, including robotic control, resource management, and even large language model prompting, often employing hierarchical models and integrating it with deep learning or other machine learning techniques to handle complex, partially observable environments. This approach offers a principled way to unify perception and action, leading to more robust, adaptable, and explainable AI systems with potential applications across various fields.