Chance-Constrained Active Inference
Abstract
Active Inference (ActInf) is an emerging theory that explains perception and action in biological agents, in terms of minimizing a free energy bound on Bayesian surprise. Goal-directed behavior is elicited by introducing prior beliefs on the underlying generative model. In contrast to prior beliefs, which constrain all realizations of a random variable, we propose an alternative approach through chance constraints, which allow for a (typically small) probability of constraint violation, and demonstrate how such constraints can be used as intrinsic drivers for goal-directed behavior in ActInf. We illustrate how chance-constrained ActInf weights all imposed (prior) constraints on the generative model, allowing e.g., for a trade-off between robust control and empirical chance constraint violation. Secondly, we interpret the proposed solution within a message passing framework. Interestingly, the message passing interpretation is not only relevant to the context of ActInf, but also provides a general purpose approach that can account for chance constraints on graphical models. The chance constraint message updates can then be readily combined with other pre-derived message update rules, without the need for custom derivations. The proposed chance-constrained message passing framework thus accelerates the search for workable models in general, and can be used to complement message-passing formulations on generative neural models.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2021
- DOI:
- 10.48550/arXiv.2102.08792
- arXiv:
- arXiv:2102.08792
- Bibcode:
- 2021arXiv210208792V
- Keywords:
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- Statistics - Machine Learning;
- Computer Science - Machine Learning;
- Computer Science - Neural and Evolutionary Computing
- E-Print:
- doi:10.1162/neco_a_01427