Successful Participation on the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) [26.09.22]
The Department of Food Informatics presented two contributions on the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) and received one award.
The conference paper "Rango: An Intuitive Rule Language for Learning Classifier Systems in Cyber-Physical Systems" by Melanie Feist (Goethe University Frankfurt a. M.) with co-authors Martin Breitbach, Heiko Trötsch (both University of Mannheim), Christian Becker (University of Stuttgart) and Christian Krupitzer (Department of Food Informatics, University of Hohenheim) was nominated as Best Paper at the IEEE International Conference on Autonomic Computing and Self-Organizing Systems (peer-reviewed; acceptance rate of 25%).
Self-adaptation is crucial for cyber-physical systems (CPS) to meet their requirements in environments characterized by complexity and uncertainty. As many situations that CPS encounter at runtime are not foreseeable at design time, (online) learning approaches are attractive for such systems. Learning classifier systems (LCS) are a promising learning approach for CPS thanks to their rather low computational complexity. They operate on a set of rules that describe potential adaptation behavior. So far, specifying rules for a learning classifier system is a tedious task that requires expert knowledge. In this paper, we present Rango - an intuitive rule language for learning classifier systems - to overcome this challenge. Compared to existing approaches, Rango has a strong focus on CPS and provides a large variety of corresponding keywords. In addition, Rango rules are automatically transferred into a representation that is usable in a learning classifier system without any modifications. Rango therefore empowers system administrators to formulate rules and, hence, leverage an online learning approach for their use case without having prior experience with learning classifier systems. We evaluate Rango extensively with (i) a complexity analysis of parsing and rule evaluation, (ii) a usefulness study which shows that Rango facilitates both the writing of rules and the understanding of LCS output and (iii) a usability study, which proves that basic programming knowledge is sufficient to understand and formulate Rango rules.
Furthermore, the poster contribution (peer-reviewed) "Towards Adaptive, Real-time Monitoring of Food Quality Using Smart Sensors" by Elia Henrichs (Department of Food Informatics, University of Hohenheim) and Christian Krupitzer was accepted and presented at the conference.
Adaptive software systems can reduce food waste and improve food safety. Such systems include smart sensors to
monitor the food’s condition and machine learning-based data analysis to predict the food’s quality and shelf life. In particular, monitoring is challenging for several reasons, e.g., the energy supply/demand and the reliability of the sensors. Therefore, this work sketches how the Multi-Level Observer/Controller architecture from Organic Computing might be applied for the adaptive monitoring of packaged foods.
The International Conference on Autonomic Computing and Self-Organizing Systems, sponsored by IEEE and founded as a merger of the IEEE International Conference on Autonomic Computing (ICAC, Core-Rating: B) and the IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), provides a forum for sharing the latest research results, ideas and experiences in autonomic computing, self-adaptation, and self-organization.
