IWAI 2020

1st International Workshop
on Active Inference

14 September 2020 in Ghent, Belgium

In Conjunction with ECML/PKDD 2020

The 1st International Workshop on Active Inference wants to bring together researchers on active inference as well as related research fields in order to discuss current trends, novel results, (real-world) applications, to what extent active inference can be used in modern machine learning settings, such as deep learning, and how it can be unified with the latest psychological and neurological insights.

Active inference is a theory of behaviour and learning that originated in neuroscience (Friston et al., 2006). The basic assumption is that intelligent agents entertain a generative model of their environment, and their main goal is to minimize surprise or, more formally, their free energy. The agents do so either by updating their generative model, so that it becomes better at explaining observations (i.e. learning), or by selecting policies that will resolve their surprise (i.e. acting), for example by moving towards prior, preferred states, or by moving towards less ambiguous states (Friston et al., 2017).

In the field of machine learning, the definition of free energy is also known as the (negative) evidence lower bound (ELBO) in variational Bayesian methods. In deep learning, this has become a popular method to build generative models of complex data using the variational autoencoder framework (Kingma et al., 2014, Rezende et al., 2014). Also, active inference has connections with the currently popular domain of reinforcement learning and intrinsic motivation (Friston et al., 2009).

Call for papers

Papers on all subjects and applications of active inference and related research areas are welcome. Topics of interest include (but are not limited to):

Important dates

  Abstract Submission Deadline: June 9, 2020
  Paper Submission Deadline: June 22, 2020
Acceptance Notification: July 9, 2020
Camera Ready Submission Deadline: TBA
Workshop Date: September 14, 2020

Paper submissions

We welcome submissions of papers with up to 6 printed pages (including references) in LNCS format (click here for details). Submissions will be evaluated according to their originality and relevance to the workshop, and should an abstract of 60-100 words. Contributions should be in PDF format and submitted via Easychair (click here).

In accordance with the main conference, will apply a double-blind review process (see also the double-blind reviewing process section below for further details). All papers need to be anonymized in the best of efforts. It is allowed to have a (non-anonymous) online pre-print. Reviewers will be asked not to search for them.

Registration

The workshop registrations will be handled by ECML/PKDD 2020 (click here). At least one author of each accepted paper should register for the conference.

Keep in mind: the early registration deadline for ECML/PKDD is July 20, 2020.

Programme

The final programme is TBA.

Organisers

Tim Verbelen, Ghent University - imec, Belgium
Cedric De Boom, Ghent University - imec, Belgium
Pablo Lanillos, Donders Institute for Brain, Cognition and Behaviour, Netherlands
Christopher Buckley, University of Sussex, United Kingdom

Programme committee

Karl Friston, University College London, United Kingdom
Philipp Schwartenbeck, University College London, United Kingdom
Rosalyn Moran, King’s College London, United Kingdom
Ayca Ozcelikkale, Uppsala University, Sweden
Christoph Mathys, SISSA Trieste - University College London, Italy - United Kingdom
Glen Berseth, University of California Berkeley, USA
Casper Hesp, University of Amsterdam, Netherlands
Tim Verbelen, Ghent University - imec, Belgium
Cedric De Boom, Ghent University - imec, Belgium
Bart Dhoedt, Ghent University - imec, Belgium
Christopher Buckley, University of Sussex, United Kingdom
Alexander Tschantz, University of Sussex, United Kingdom
Maxwell Ramstead, McGill University, Canada
Pablo Lanillos, Donders Institute for Brain, Cognition and Behaviour, Netherlands

References

Karl Friston, James Kilner, Lee Harrison. A free energy principle for the brain. Journal of Physiology-Paris, Volume 100, Issues 1–3, 2006.

Karl J. Friston, Jean Daunizeau, and Stefan J. Kiebel. Reinforcement Learning or Active Inference? PLoS ONE, 4(7), 2009.

Karl J. Friston, Marco Lin, Christopher D. Frith, Giovanni Pezzulo, J. Allan Hobson, and Sasha Ondobaka. Active Inference, Curiosity and Insight. Neural Computation, 29(10): 2633–2683, 2017.

Diederik P. Kingma and Max Welling. Auto-encoding variational bayes. 2nd International Conference on Learning Representations, 2014.

Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. 31st International Conference on International Conference on Machine Learning, 2014.