View from Certosa di Pontignano

As previously announced, last week I had the pleasure to present our joint work with our partners from Ruhr-University Bochum on explainable reinforcement learning at the 8th Annual Conference on machine Learning, Optimization and Data science (LOD). The presentation sparked interesting questions and lead to inspiring discussions in the enchanting ambiance of the medieval monastery in Tuscany.
As the conference was held in conjunction with the Advanced Course & Symposium on Artificial Intelligence & Neuroscience (ACAIN), we could profit from a very stimulating interdisciplinary environment with talks, tutorials, and posters covering topics reaching from the biology of neuronal development to implementation details of different deep learning frameworks.
We are looking forward to LOD 2023!

 

The second edition of the annual Dataninja-Retreat took place this September in Tecklenburg. The different Dataninja projects presented their proceedings, we had the pleasure of attending a lecture by Prof. Xiaoyi Jiang, and fresh PhD graduates of the KI-Starter-project kindly shared experiences and some tips from their recently concluded PhD-journey. Last but not least it was of course a very pleasant and rare occasion for seeing each other offline, exchanging ideas, experiences, struggles, and successes.

Group picture Dataninja Retreat 2022

Group picture of the participants

We are pleased to announce that we will present our research on explainable reinforcement learning at the 8th Annual Conference on machine Learning, Optimization and Data science (LOD).

Carthusian monastery in Pontignano Siena, Italy

Carthusian monastery in Pontignano Siena, Italy. Venue of LOD 2022

Starting with its first edition in 2015, the LOD is an established international and interdisciplinary forum for research and discussion of Deep Learning, Optimization, Big Data, and Artificial Intelligence. This year's 8th edition of the will be held online and onsite in Pontignano near Siena, Italy on September 18th - 22nd 2022.

Reading the conference's manifesto “The problem of understanding intelligence is said to be the greatest problem in science today and ‘the’ problem for this century” we find this prestigious conference to be the perfect place to present our work targeted at making deep reinforcement agents explainable.
We are very grateful for the opportunity to present our paper titled “Sample-based Rule Extraction for Explainable Reinforcement Learning”, which outlines the results of our ongoing research of inducing simple, transparent, human-readable rules from well-trained deep reinforcement learning agents. A link to the article will be added as soon as it is published. For those interested, early registration for the conference is available until Sunday July 31st, 2022.

Thumbnail Poster "Shedding Light into the Black Box of Reinforcement Learning"In September 2021, shortly after the Dataninja retreat, we participated at the KI 2021 44th German Conference on Artificial Intelligence.

Alongside fellow members of the Dataninja research training group we, Raphael Engelhardt and Wolfgang Konen from TH Köln together with Laurenz Wiskott and Moritz Lange from RUB Bochum, presented our work on rule extraction from trained reinforcement learning agents in a poster session as part of the workshop "Trustworthy AI in the wild". The conference had to be held virtually which did not affect some very interesting discussions and exchange of new ideas.

Our poster as well as the extended abstract are available online.


Between the third and fourth wave of the COVID-19 pandemic, we were in September 2021 lucky enough to hold in presence the annual retreat of the Dataninja research group, which was at the same time the group's first ever in-person meeting. The rich and balanced program included presentations of the different Dataninja projects by the respective PhD candidates, scientific talks by guest speakers, networking and outdoor activities in the nature surrounding Willingen.

While the science was top notch, there is definitely room for improvement regarding steering a canoe as the following tracking picture of one of the canoes shows 🙂 ...