10 June 2024

EurAI, a representative body of the European AI community, has awarded LiU researcher Mattias Tiger for his doctoral thesis. He may be the first Swedish researcher to receive such an award.

Two men at a stage shaking hand and smiling against the public, one of them holding a paper
On stage at the ECAI AI conference in Krakow, Poland, which is organized by the European association for AI, EurAI, Mattias Tiger received a diploma from EurAI's president Carles Sierra. 

Mattias Tiger's doctoral thesis was highly praised “for ensuring that real world deployment of robots and autonomous systems can take place safely”. He may be the first LiU researcher to be awarded such an award by the European association for AI, EurAI. Perhaps even the first Swedish researcher.

– I am very honored to have been awarded the diploma for my research contributions. It is a significant international recognition of LiU's relevance on the academic AI scene.

Två män står vid en roll up på konferens

Mattias Tiger, here at ECAI in the company of LiU professor Fredrik Heintz, also took the opportunity at the conference to talk to many AI researchers who showed up from all over the world.

– EurAI, the European Association for AI, brings together all national AI organisations and organises the annual European AI conference ECAI. EurAI's award to Mattias shows that his research really stands out. It is impressive, says professor Fredrik Heintz.


About the award, EurAI and ECAI

EurAI stands for European Association for Artificial Intelligence. Read more about Mattias Tiger's award at the EurAI webpage.

ECAI is organised by EurAI and is the largest AI conference in Europe, according to Fredrik Heintz. Read more about the upcoming conference at the ECAI webpage.

About the PhD thesis

Safety-Aware Autonomous Systems: Preparing Robots for Life in the Real World

From Mattias Tiger's PhD thesis:

Real‐world autonomous systems are expected to be increasingly deployed and operating in real‐world environments over the coming decades. Autonomous systems such as AI‐enabled robotic systems and intelligent transportation systems, will alleviate mundane human work, provide new services, and facilitate a smarter and more flexible infrastructure. The real‐world environments affected include workplaces, public spaces, and homes.

To ensure safe operations, in for example the vicinity of people, it is paramount that the autonomous systems are explainable, behave predictable, and can handle that the real world is ever changing and only partially observable.

To deal with a dynamic and changing environment, consistently and safely, it is necessary to have sound uncertainty management. Explicit uncertainty quantification is fundamental to providing probabilistic safety guarantees that can also be monitored during runtime to ensure safety in new situations. It is further necessary for well‐grounded prediction and classification uncertainty, for achieving task effectiveness with high robustness and for dealing with unknown unknowns, such as world model divergence, using anomaly detection.

This dissertation focuses on the notion of motion in terms of trajectories, from recognizing – to anticipating – to generating – to monitoring that it fulfills expectations such as predictability or other safety constraints during runtime. Efficiency, effectiveness, and safety are competing qualities, and in safety critical applications the required degree of safety makes it very challenging to reach useful levels of efficiency and effectiveness. To this end, a holistic perspective on agent motion in complex and dynamic environments is investigated. This work leverage synergies in well‐founded formalized interactions and integration between learning, reasoning, and interaction, and demonstrate jointly efficient, effective, and safe capabilities for autonomous systems in safety‐critical situations.

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by grants from the National Graduate School in Computer Science (CUGS), the Swedish Foundation for Strategic Research (SSF) project CUAS, the Swedish Research Council (VR) Linnaeus Center CADICS, Sweden, the Center for Industrial Information Technology CENIIT, the Excellence Center at Linkping‐Lund for Information Technology (ELLIIT), the TAILOR Project funded by EU Horizon 2020 research and innovation programme GA No 952215, and Knut and Alice Wallenberg Foundation (KAW 2019.0350).

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