In order to assess the feasibility of active inference as a framework for synthetic agents in a real-world setting, we developed a ground-based robot that needs to learn to navigate to an undisclosed parking location. The robot can only learn where to park through situated interactions with a human observer who is aware of the target location.
ForneyLab is a novel Julia package that allows the user to specify a probabilistic model as an FFG and pose inference problems on this FFG. ForneyLab is especially potent when applied to time-series data, where it attains comparable performance to Stan and Edward in significantly less computation time.
We develop a fully probabilistic approach to pure-tone audiometry. By assuming a Gaussian process based response model for test tones, the hearing threshold estimation problem becomes one of Bayesian inference. This allows the use of information-theoretic criteria to select optimal test tones.
The primary mission of the BIASlab team is to develop in-situ trainable Bayesian Intelligent Agents for applications to wearable technology.
In this project you will study how two active inference agents can cooperate to solve a task.
In this project you will simulate a Bayesian machine learning algorithm on a neuromorphic chip.
In this project you will develop an agent to infer the dynamical parameters of an electro-mechanical positioning system.