Author/Contact: Lukas Esterle


Autonomous systems have to respond to their dynamic environment and even rapidly unfolding situations. In all cases, an awareness of these systems about their own capabilities, goals, and resources is fundamental for making profound and meaningful decisions. The awareness of these different aspects in modern systems is often still hard-coded. Future autonomous systems, however, will be required to deal with changing environments and with the fact that they have to adapt to those changes over time, exploring and developing new capabilities, utilising different resources from their environment, and establish interactions and collaborations with others during runtime.

This study topic specifically explores computational self-awareness from different angles:

  1. Different degrees and levels of self-awareness: Computational self-awareness will enable autonomous systems to become aware of stimuli, time, its own goals, its capabilities, and resources, among other aspects. This represents a varying complexity of awareness, giving rise to a trade-off between complexity in capturing and retaining this information and utilising it. 
  2. Approaches for self-awareness: Different approaches and methods can be utilised to make autonomous systems self-aware. This angle sheds light on these algorithms, as well as their benefits and shortcomings.
  3. Self-awareness at different levels: As computational self-awareness itself requires resources, of which future autonomous systems might be limited. Carefully selecting what a system should be self-aware about and how it should be utilised will be essential. Autonomous systems relying heavily on sensor information and need to be aware when certain sensors fail while other systems might require to have an awareness of their current situation among other systems to make informed decisions. Future systems will even be able to change their awareness based on their current goals.
  4. Networked self-awareness: Many autonomous systems are embedded among other (autonomous) systems and humans. While communication protocols allow for exchange of information, not all systems are built and enabled to communicate with each other. This requires future autonomous systems to extend their self-awareness towards other systems in their environment and their respective impact they have on them. This can again be considered from different levels (e.g., what actions are performed by other systems or what are the underlying goals of other systems in my environment). Having such an understanding will allow for better interactions with less conflicts among systems operating in a shared environment.

You might be interested to explore more on the topic:

ASI Forum board member in charge: Lukas Esterle
Lukas Esterle received his MSc (Dipl.-Ing.) and PhD (Dr.-techn.) from the University of Klagenfurt, Austria in 2010 and 2014 respectively. Afterwards he was a Post-doctoral researcher at Vienna University of Technology before becoming a Marie Skłodowska-Curie fellow at Aston University and the University of Birmingham, UK. In 2019 he joined Aarhus University, Denmark, where he is now an Associate Professor and leads the Autonomous Intelligent Systems research theme. He authored numerous papers, articles and book chapters on computational self-awareness, autonomous and self-adaptive systems, and collaborative behaviour. He is a member of the DIGIT Aarhus University Centre for Digitalisation, Big Data and Data Analytics, the steering committee of the International Conference on Autonomic Computing and Self-organizing Systems (ACSOS), and area chair for IEEE Flagship Conference on Multimedia and Expo (ICME).

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