Natural Artificial Intelligence

Automating the development of novel signal processing and control systems through neuro-inspired free-energy minimizing agents

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Natural Artificial Intelligence

Automating the development of novel signal processing and control systems through neuro-inspired free-energy minimizing agents

Find out more

Projects

Simulating Football Players’ Decision-Making Process

The goal of this project was to build an assistive tool that could help Football coaches to simulate the actions of an “optimally behaving” defensive team. We developed a cost function that optimizes both Pitch Control and Pass Interceptions for the defensive team.

Learning Where to Park by Active Inference

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: a Julia Toolbox for Factor Graph-Based Probabilistic Programming

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.

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Publications

Bayesian Pure-Tone Audiometry Through Active Learning Under Informed Priors

Marco Cox , Bert de Vries

Frontiers in Digital Health (2021)

August, 2021

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Variational Log-Power Spectral Tracking for Acoustic Signals

Bart van Erp , Ismail Senoz , Bert de Vries

SSP 2021

July, 2021

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Extended Variational Message Passing for Automated Approximate Bayesian Inference

Semih Akbayrak , Ivan Bocharov , Bert de Vries

Entropy

June, 2021

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Variational Message Passing and Local Constraint Manipulation in Factor Graphs

Ismail Senoz , Thijs van de Laar , Dmitry V. Bagaev , Bert de Vries

Entropy

June, 2021

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Message Passing-Based Inference for Time-Varying Autoregressive Models

Albert Podusenko , Wouter Kouw , Bert de Vries

Entropy

May, 2021

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Blog

Our Mission: Bayesian Intelligent Agents

The primary mission of the BIASlab team is to develop in-situ trainable Bayesian Intelligent Agents for applications to wearable technology.

Our Approach to Solving Difficult Design Problems

How do you solve a problem that is only vaguely described? We describe here an engineering approach that guides our research on solving vaguely defined problems such as hearing impairment.