Message-Passing

This talk presents a principled mathematical framework for deriving information-seeking behavior in intelligent agents, grounded in Bayesian inference and the free energy principle.

We present the design of an autoregressive active inference agent in the form of message passing on a factor graph.

We propose a variational message passing inference algorithm for online system identification using polynomial NARMAX models. We show empirically that our variational Bayesian estimator outperforms an online recursive least-squares estimator, most notably in small sample size settings and low noise regimes, and performs on par with an iterative least-squares estimator trained offline.

Message passing on factor graphs typically relies on a scheduling procedure, in which a central algorithm or compiler figures out which nodes should pass messages where at what time. This is not a biologically plausible mechanism. I explore the possibility of passing messages without a scheduler, where the nodes merely “react” to incoming messages.