Multi-Modal and Multi-Factor Branching Time Active Inference

by   Théophile Champion, et al.

Active inference is a state-of-the-art framework for modelling the brain that explains a wide range of mechanisms such as habit formation, dopaminergic discharge and curiosity. Recently, two versions of branching time active inference (BTAI) based on Monte-Carlo tree search have been developed to handle the exponential (space and time) complexity class that occurs when computing the prior over all possible policies up to the time horizon. However, those two versions of BTAI still suffer from an exponential complexity class w.r.t the number of observed and latent variables being modelled. In the present paper, we resolve this limitation by first allowing the modelling of several observations, each of them having its own likelihood mapping. Similarly, we allow each latent state to have its own transition mapping. The inference algorithm then exploits the factorisation of the likelihood and transition mappings to accelerate the computation of the posterior. Those two optimisations were tested on the dSprites environment in which the metadata of the dSprites dataset was used as input to the model instead of the dSprites images. On this task, BTAI_VMP (Champion et al., 2022b,a) was able to solve 96.9% of the task in 5.1 seconds, and BTAI_BF (Champion et al., 2021a) was able to solve 98.6% of the task in 17.5 seconds. Our new approach (BTAI_3MF) outperformed both of its predecessors by solving the task completly (100%) in only 2.559 seconds. Finally, BTAI_3MF has been implemented in a flexible and easy to use (python) package, and we developed a graphical user interface to enable the inspection of the model's beliefs, planning process and behaviour.


page 11

page 22

page 23

page 24


Branching Time Active Inference: empirical study and complexity class analysis

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

Branching Time Active Inference: the theory and its generality

Over the last 10 to 15 years, active inference has helped to explain var...

Branching Time Active Inference with Bayesian Filtering

Branching Time Active Inference (Champion et al., 2021b,a) is a framewor...

Dimension Correction for Hierarchical Latent Class Models

Model complexity is an important factor to consider when selecting among...

Fast and robust Bayesian Inference using Gaussian Processes with GPry

We present the GPry algorithm for fast Bayesian inference of general (no...

Mapping Husserlian phenomenology onto active inference

Phenomenology is the rigorous descriptive study of conscious experience....

Planning to Learn: A Novel Algorithm for Active Learning during Model-Based Planning

Active Inference is a recent framework for modeling planning under uncer...

Please sign up or login with your details

Forgot password? Click here to reset