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Inferring in Circles: Active Inference in Continuous State Space Using Hierarchical Gaussian Filtering of Sufficient Statistics

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)


We create a continuous state space active inference agent based on the hierarchical Gaussian filter. It uses the HGF to track the sufficient statistics of noisy observations of a moving target that is performing a Gaussian random walk with drift and varying volatility. On the basis of this filtering, the agent predicts the target’s position, and minimizes surprisal by staying close to it. Our simulated agent represents the first full implementation of this approach. It demonstrates the feasibility of supplementing active inference with HGF-filtering of the sufficient statistics of observations, which is particularly useful in noisy and volatile continuous state space environments.

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Correspondence to Peter Thestrup Waade .

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Waade, P.T., Mikus, N., Mathys, C. (2021). Inferring in Circles: Active Inference in Continuous State Space Using Hierarchical Gaussian Filtering of Sufficient Statistics. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1524. Springer, Cham.

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