Safe, intelligent and explainable self-adaptive systems

Intelligent cyber-physical systems, such as self-driving cars, smart homes or e-health solutions, will increasingly influence our daily lives. They will deal with increasingly uncertain and changing environments and simultaneously must adhere to strict safety requirements. In addition, we need to trust those systems, as we will hand over control on our daily life to them. This increasing list of non-functional requirements makes the design of intelligent cyber-physical systems (CPS) a challenging task. In this thesis, we advance research on intelligent CPS by introducing intelligent self-adaptive systems that autonomously evolve their adaptation logic in response to changes in the system structure, their environment and their goals. We make their widespread integration possible, by introducing a safe design framework, based on a novel methodology. Our framework enables the integrated design and formal verification of intelligent and explainable self-adaptive systems. Our key idea is to combine a resource-efficient process for self-adaptation with dynamic evolution of the adaptation logics and continuous verification activities. To obtain trust in those systems, we additionally collect structured runtime knowledge to build an explanation basis for autonomous decisions. Our main contributions are an efficient and comprehensible rule- and distance-based adaptation process, a quantitative and context-dependent goal model that provides the basis for our adaptation process, a resource-efficient run-time evolution of adaptation logics that combines a continuous evaluation and observation-based optimization, of adaptation rules and a stochastic search-based learning of new comprehensible adaptation rules, and a continuous verification methodology that is based on a formalization of our rule- and distance-based adaptation process in timed automata. We have implemented our framework and evaluated its applicability and performance on three case studies from different domains, namely a smart temperature control system, an autonomous drone delivery system and a self-organizing production system. With our framework, we support the design of systems that are flexible enough to deal with dynamically changing operation contexts, and, at the same time, provide safety assurances and explainability of their autonomous decisions. It is the only approach that provides an integrated solution to this crucial research topic.

Author dc:creator

• Klös, Verena

Advisor dc:contributor.advisor

• Glesner, Sabine

Licence dc:rights.uri

• https://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Identifier URI

http://dx.doi.org/10.14279/depositonce-11143

OAI identifier oai:identifier

oai:depositonce.tu-berlin.de:11303/12267