Active Inference
Overview
Active inference models agents as maintaining beliefs about hidden states and choosing actions that both meet preferred outcomes and reduce uncertainty. Common approaches use probabilistic generative models, variational message passing, and expected-free-energy planning, with extensions for hierarchical control, learned world models, and allocation of sensory precision. Researchers apply these ideas to robotics, navigation, social interaction, health, and biological behavior, evaluating them in tasks such as mazes, control simulations, and real-world systems. A central open tension is how to define and calibrate epistemic value so information seeking supports reliable goal achievement without excessive exploration, while keeping inference scalable and models testable.
Latest papers
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