Chance-Constrained Active Inference

• URL: http://arxiv.org/abs/2102.08792v1
• Date: Wed, 17 Feb 2021 14:36:40 GMT
• Title: Chance-Constrained Active Inference
• Authors: Thijs van de Laar, Ismail Senoz, Ay\c{c}a \"Oz\c{c}elikkale, Henk Wymeersch
• Abstract summary: Active Inference (ActInf) is an emerging theory that explains perception and action in biological agents. We propose an alternative approach through chance constraints, which allow for a (typically small) probability of constraint violation. We show how chance-constrained ActInf weights all imposed (prior) constraints on the generative model, allowing a trade-off between robust control and empirical chance constraint violation.
• Score: 21.592135424253826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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