Abstract
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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References
Çatal, O., Wauthier, S., De Boom, C., Verbelen, T., Dhoedt, B.: Learning generative state space models for active inference. Front. Comput. Neurosci. 14, 103 (2020)
Friston, K.: A free energy principle for a particular physics. arXiv preprint arXiv:1906.10184 (2019)
Friston, K., Adams, R., Perrinet, L., Breakspear, M.: Perceptions as hypotheses: saccades as experiments. Front. Psychol. 3, 151 (2012)
Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O’Doherty, J., Pezzulo, G.: Active inference and learning. Neurosci. Biobehav. Rev. 68, 862–879 (2016). https://doi.org/10.1016/j.neubiorev.2016.06.022
Friston, K., Frith, C.: A duet for one. Conscious. Cogn. 36, 390–405 (2015)
Friston, K., Kiebel, S.: Predictive coding under the free-energy principle. Philos. Trans. R. Soc. B Biol. Sci. 364(1521), 1211–1221 (2009)
Friston, K.J., Daunizeau, J., Kiebel, S.J.: Reinforcement Learning or Active Inference? PLoS ONE 4(7), e6421 (2009). https://doi.org/10.1371/journal.pone.0006421
Friston, K.J., Daunizeau, J., Kilner, J., Kiebel, S.J.: Action and behavior: a free-energy formulation. Biol. Cybern. 102(3), 227–260 (2010). https://doi.org/10.1007/s00422-010-0364-z
Friston, K.J., Parr, T., de Vries, B.: The graphical brain: belief propagation and active inference. Netw. Neurosci. 1(4), 381–414 (2017)
Friston, K.J., Sajid, N., Quiroga-Martinez, D.R., Parr, T., Price, C.J., Holmes, E.: Active listening. Hear. Res. 399, 107998 (2021)
Van de Maele, T., Verbelen, T., Çatal, O., De Boom, C., Dhoedt, B.: Active vision for robot manipulators using the free energy principle. Front. Neurorobotics 15, 14 (2021)
Mathys, C., Daunizeau, J., Friston, K.J., Stephan, K.E.: A Bayesian foundation for individual learning under uncertainty. Front. Hum. Neurosci. 5, 39 (2011). https://doi.org/10.3389/fnhum.2011.00039
Mathys, C., et al.: Uncertainty in perception and the hierarchical gaussian filter. Front. Hum. Neurosci. 8, 825 (2014). https://doi.org/10.3389/fnhum.2014.00825
Mathys, C., Weber, L.: Hierarchical gaussian filtering of sufficient statistic time series for active inference. In: IWAI 2020. CCIS, vol. 1326, pp. 52–58. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64919-7_7
Parr, T., Friston, K.J.: The discrete and continuous brain: from decisions to movement-and back again. Neural Comput. 30(9), 2319–2347 (2018)
Şenöz, İ., De Vries, B.: Online variational message passing in the hierarchical gaussian filter. In: 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. IEEE (2018)
Şenöz, İ., de Vries, B.: Online message passing-based inference in the hierarchical gaussian filter. In: 2020 IEEE International Symposium on Information Theory (ISIT), pp. 2676–2681. IEEE (2020)
Smith, R., Friston, K., Whyte, C.: A step-by-step tutorial on active inference and its application to empirical data. PsyArXiv (2021)
Tschantz, A., Baltieri, M., Seth, A.K., Buckley, C.L.: Scaling active inference. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2020)
Weber, Lilian, A.E.: Perception as Hierarchical Bayesian Inference - Toward Non-Invasive Readouts of Exteroceptive and Interoceptive Processing. Doctoral thesis, ETH Zurich (2020)
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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. https://doi.org/10.1007/978-3-030-93736-2_57
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DOI: https://doi.org/10.1007/978-3-030-93736-2_57
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