Chance-Constrained Active Inference

by   Thijs van de Laar, et al.

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.



There are no comments yet.


page 1

page 2

page 3

page 4


Active Inference and Epistemic Value in Graphical Models

The Free Energy Principle (FEP) postulates that biological agents percei...

Consensus Message Passing for Layered Graphical Models

Generative models provide a powerful framework for probabilistic reasoni...

Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker

Active inference is a state-of-the-art framework in neuroscience that of...

AIDA: An Active Inference-based Design Agent for Audio Processing Algorithms

In this paper we present AIDA, which is an active inference-based agent ...

A Probabilistic Modeling Approach to Hearing Loss Compensation

Hearing Aid (HA) algorithms need to be tuned ("fitted") to match the imp...

Branching Time Active Inference: empirical study and complexity class analysis

Active inference is a state-of-the-art framework for modelling the brain...

Covert Message Passing over Public Internet Platforms Using Model-Based Format-Transforming Encryption

We introduce a new type of format-transforming encryption where the form...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.