Currently the research is structured in two parts:

  • Autonomous Systems and Software (WASP-AS)

The Autonomous Systems and Software part of WASP can be described along two main dimensions: a strategic dimension and a thematic dimension. The strategic perspective emphasizes areas of impact on individuals, society, and industry, whereas the aim of the thematic areas is to encapsulate the underlying scientific and technological challenges that are common to all types of autonomous systems.

The individual PhD student projects in WASP-AS are grouped into ten clusters. Two of these clusters – Perception and Learning in Interactive Autonomous Systems and AI and Machine Learning – also contain AI and ML projects.

  • Artificial Intelligence (WASP-AI)

The focus on latter part is artificial intelligence (AI) in the broadest sense, but with two main focuses. The larger of these two is Machine Learning (ML), Deep Learning (DL) and other AI. For this part we use the abbreviation AI/MLX where X stands for other AI, but is also commonly used for eXplainable AI (XAI). In recent years, we have seen amazing examples of achievements in Machine Learning, especially Deep Learning, and XAI is a complement asking the system how it came to its response, if the system can motivate its response and perhaps generalize its response. This part, X, typically contains classical questions within the AI, and is here viewed in the perspective of interaction with ML and DL.

The second major focus is mathematics to attack the theoretical basic questions of AI in the broadest sense. As in the case of ML and DL, recent years have emerged new mathematics, which is of great importance both for understanding and applications. The expansion of WASP is not a general focus on mathematics but focuses on the new math that forms the basis of AI. We use the notation AI/MATH.