BaCAI Lectures

Multi-agent Pathfinding on Directed Graphs

Multi-agent pathfinding (MAPF) is the problem of deciding the existence of or generating a collision-free movement plan for a set of agents moving on a graph. Because of its relevance to topics such as warehouse logistics, air traffic coordination, and video games, this problem has received much interest lately.

After a survey about theoretical results and algorithmic approaches that have been employed, I will focus on the variant where the graph is directed. While the non-optimizing variant of multi-agent pathfinding on undirected graphs has been known to be a polynomial-time problem for almost forty years, a similar result had not been established for directed graphs. Only recently has it been shown that this problem is NP-complete. However, the problem is polynomial for strongly connected directed graphs. And both of these results hold even if one allows for synchronous rotations on fully occupied cycles.

About Prof. Dr. Bernhard Nebel

Bernhard Nebel received his first degree in Computer Science (Dipl.-Inform.) from the University of Hamburg in 1980 and his Ph.D. (Dr. rer. nat.) from the University of Saarland in 1989. Between 1982 and 1993 he worked on different AI projects at the University of Hamburg, the Technical University of Berlin, ISI/USC, IBM Germany, and the German Research Center for AI (DFKI). From 1993 to 1996 he held an Associate Professor position (C3) at the University of Ulm. Between 1996 and 2022 he has been Professor at Albert-Ludwigs-Universit?t Freiburg and head of the research group on Foundations of Artificial Intelligence. Since 2022, he is retired.

Bernhard Nebel was a member of the the collaborative research center SFB TR/14 Automatic Verification and Analysis of Complex Systems (AVACS) (2004-2015), and he was coordinator of the Freiburg site of the collaborative research center SFB TR/8 Spatial Cognition (2003-20014).

Among other professional services, he served as the Program Co-chair for the 3rd International Conference on Principles of Knowledge Representation and Reasoning (KR'92), as the Program Co-chair for the 18th German Annual Conference on AI (KI'94), as the General Chair of the 21st German Annual Conference on Artificial Intelligence (KI'97), as the Program Chair for the 17th International Joint Conference on Artificial Intelligence (IJCAI'01), and as Conference Co-Chair of the 18th International Conference on Automated Planning and Scheduling (ICAPS'08). In 2001, Bernhard Nebel was elected as an ECCAI fellow. In 2009, he was elected to be a member of the German Academy of Science Leopoldina. In 2010, Bernhard Nebel was elected as an AAAI fellow, and in 2011 he became a member of Academia Europaea. In 2019, he was mentioned as one of the 10 formative researchers of the German AI history, and since 2022, he is an ACM Fellow. In 2022, he also received the Donald E. Walker Distinguished Service Award.

Bernhard Nebel is (co-)author and (co-)editor of 14 books and conference proceedings, as well as author and co-author of more than 200 refereed papers in scientific journals, books, and conference proceedings.

A cautionary tale of health AI development and deployment

This talk will cover different challenges that arise when developing and deploying AI algorithms at scale across different geographies. Generative AI, in particular, holds a lot of promise for reducing barriers to access and advancing quality of healthcare around the world. It is more important than ever, though, to ensure that these algorithms are properly validated and tested and sufficient investment is made to ensure that their benefits are equally distributed across different populations.

About Ira Ktena, PhD

Ira Ktena is a Staff Research Scientist at Google DeepMindworking on Safe and Reliable Machine Learning research. Previously, she was a senior Machine Learning Researcher with the Cortex Applied Research team at Twitter UK, focusing on real-time personalisation while she carried out research at the intersection of recommender systems and algorithmic transparency. Their exploration on algorithmic amplification of political content on Twitter was featured by the Economist and the BBC, among others. 

Why do LLMs need to worry?

Lecture in English

The Achilles heel of Natural Language Processing research has for a long time been the noise in its datasets, in particular inaccurate labels. While there has been a paradigm shift from small expert models trained on labelled datasets to large language models (LLMs) trained by largely unlabelled data, which are capable of solving a variety of tasks, the problems of overconfidence and bias persist. In this talk I will present some methods for estimating uncertainty in task-oriented dialogue and utilising it to automatically correct the labels in the underlying datasets. I will hypothesise how this may open the door to solving related issues in LLMs.?

About Prof. Dr. Milica Ga?i?:

Milica Ga?i? is a Professor of the Dialog Systems and Machine Learning Group at the Heinrich Heine University Düsseldorf. Her research focuses on fundamental questions of human-computer dialogue modelling and lie in the intersection of Natural Language Processing and Machine Learning. Prior to her current position she was a Lecturer in Spoken Dialogue Systems at the Department of Engineering, University of Cambridge where she was leading the Dialogue Systems Group. Previously, she was a Research Associate and a Senior Research Associate in the same group and a Research Fellow at Murray Edwards College. She completed her PhD under the supervision of Professor Steve Young and the topic of her thesis was Statistical Dialogue Modelling for which she received an EPSRC PhD Plus Award. She holds an MPhil degree in Computer Speech, Text and Internet Technology from the University of Cambridge and Diploma (BSc. equivalent) in Mathematics and Computer Science from the University of Belgrade. She is a member of ACL, a member of ELLIS and a senior member of IEEE as well as a member of the International Scientific Advisory Board of DFKI.

Probabilistic and Deep Learning Techniques for Robot Navigation and Automated Driving

For autonomous robots and automated driving, the capability to robustly perceive environments and execute their actions is the ultimate goal. The key challenge is that no sensors and actuators are perfect, which means that robots and cars need the ability to properly deal with the resulting uncertainty. In this presentation, I will introduce the probabilistic approach to robotics, which provides a rigorous statistical methodology to deal with state estimation problems. I will furthermore discuss how this approach can be combined using state-of-the-art technology from machine learning to deal with complex and changing real-world environments.

About Prof. Dr. Wolfram Burgard

Prof. Dr. Wolfram Burgard is a distinguished Professor of Robotics and Artificial Intelligence at the University of Technology Nuremberg where he also serves as Founding Chair of the Engineering Department. Previously, he held the position of Professor of Computer Science at the University of Freiburg from 1999 to 2021, where he established the renowned research lab for Autonomous Intelligent Systems.  His expertise lies in artificial intelligence and mobile robots, focusing on the development of robust and adaptive techniques for state estimation and control. Wolfram Burgard's achievements include deploying the first interactive mobile tour-guide robot, Rhino, at the Deutsches Museum Bonn in 1997. He and his team also developed a groundbreaking approach that allowed a car to autonomously navigate through a complex parking garage and park itself in 2008. In 2012, he and his team developed the robot Obelix that autonomously navigated like a pedestrian from the campus of the Faculty of Engineering to the city center of Freiburg.  Wolfram Burgard has published over 350 papers and articles in robotic and artificial intelligence conferences and journals. Additionally, he co-authored the two books “Principles of Robot Motion - Theory, Algorithms, and Implementations” and “Probabilistic Robotics”. In 2009, he was honored with the Gottfried Wilhelm Leibniz Prize, the most prestigious research award in Germany.  He is a member of the Heidelberg Academy of Sciences and the German Academy of Sciences Leopoldina.

Particularity and Challenges of Machine Learning for Intra-operative Imaging

Towards precision and Intelligence in high intensity, dynamic environments

Lecture in English

Over the past decade, the rapid advancements in machine learning (ML) have revolutionized various fields, significantly impacting our lives. In this talk, we will delve into the realm of medical applications and explore the challenges and opportunities associated with integrating these cutting-edge technologies into computer-assisted interventions. Our primary focus will be on fostering acceptance and trust in machine learning and robotic solutions within the medical domain, which often necessitates the path through Intelligence Amplification (IA). Augmented Reality (AR) allows us to leverage IA to augment human intelligence and expertise, ultimately paving the way for the seamless integration of Artificial Intelligence (AI) and robotics into clinical solutions.

Drawing from some groundbreaking research conducted at the Chair of Computer-Aided Medical Procedures at both TU Munich and Johns Hopkins Universities, I will present a series of novel techniques developed to address the unique demands of medical applications. Specifically, we will explore their practical implementations in diverse areas, including Robotic Ultrasound Imaging, Multimodal Data Analysis, and Semantic Scene Graphs for Holistic Modeling of Surgical Domain. Furthermore, I will showcase compelling examples of how AR-solutions can serve as catalysts for embracing AI in computer-assisted surgery. By harnessing the power of IA, we can unlock the full potential of AI technologies, bolstering acceptance and driving the future of computer-assisted interventions. Join me on this enlightening journey as we navigate the intricate intersection of machine learning, medical advancements, and the path from intelligence amplification to artificial intelligence in healthcare.

About Prof. Dr. Nassir Navab

Prof. Dr. Nassir Navab is a full professor and director of the Laboratories for Computer Aided Medical Procedures (CAMP) at Technical University of Munich (TUM) and an adjunct Professor at Johns Hopkins University. He is also the director of biannual Medical Augmented Reality school series at Balgrist Hospital in Zurich. He is a Member of Academia Europaea and received the prestigious MICCAI Enduring Impact Award in 2021 and IEEE ISMAR 10 Years Lasting Impact Award in 2015. In 2001, while acting as distinguished member of technical staff at Siemens Corporate Research (SCR) in Princeton, he received the prestigious Siemens Inventor of the Year Award for the body of his work in interventional imaging. He also received the SMIT Technology Innovation Award in 2010. He is also honored as Medical AR Pioneer in AWE XR hall of fame in 2024. His students have received many paper awards including 15 awards at the prestigious MICCAI events, 5 at IPCAI, 2 at IPMI and 4 at IEEE ISMAR. He is Fellow of the MICCAI Society and acted on its board of directors from 2007 to 2012 and from 2014 to 2017. He is also an IEEE Fellow and Fellow of Asia-Pacific Artificial Intelligence Association (AAIA) and one of the founders of IEEE Symposium on Mixed and Augmented Reality (ISMAR) and has been serving on its Steering Committee since 2001. He is an Area Chair for ECCV 2024. He is the author of hundreds of peer reviewed scientific papers and over 100 granted US and international patents. As of April 2024, his papers have received over 76,600 citations and enjoy an h-index of 117.