Trustworthy Autonomous Systems Node in Resilience

Imagine a future where autonomous systems are widely available to improve our lives. In this future, autonomous robots unobtrusively maintain the infrastructure of our cities, and support people in living fulfilled independent lives. In this future, autonomous software reliably diagnoses disease at early stages, and dependably manages our road traffic to maximise flow and minimise environmental impact. Before this vision becomes reality, several major limitations of current autonomous systems need to be addressed. Key among these limitations is their reduced resilience: today's autonomous systems cannot avoid, withstand, recover from, adapt, and evolve to handle the uncertainty, change, faults, failure, adversity, and other disruptions present in such applications. Recent and forthcoming technological advances will provide autonomous systems with many of the sensors, actuators and other functional building blocks required to achieve the desired resilience levels, but this is not enough. To be resilient and trustworthy in these important applications, future autonomous systems will also need to use these building blocks effectively, so that they achieve complex technical requirements without violating our social, legal, ethical, empathy and cultural (SLEEC) rules and norms. Additionally, they will need to provide us with compelling evidence that the decisions and actions supporting their resilience satisfy both technical and SLEEC-compliance goals.

To address these challenging needs, our project will develop a comprehensive toolbox of mathematically based notations and models, SLEEC-compliant resilience-enhancing methods, and systematic approaches for developing, deploying, optimising, and assuring highly resilient autonomous systems and systems of systems. To this end, we will capture the multidisciplinary nature of the social and technical aspects of the environment in which autonomous systems operate - and of the systems themselves - via mathematical models. For that, we have a team of Computer Scientists, Engineers, Psychologists, Philosophers, Lawyers, and Mathematicians, with an extensive track record of delivering research in all areas of the project. Working with such a mathematical model, autonomous systems will determine which resilience- enhancing actions are feasible, meet technical requirements, and are compliant with the relevant SLEEC rules and norms. Like humans, our autonomous systems will be able to reduce uncertainty, and to predict, detect and respond to change, faults, failures and adversity, proactively and efficiently. Like humans, if needed, our autonomous systems will share knowledge and services with humans and other autonomous agents. Like humans, if needed, our autonomous systems will cooperate with one another and with humans, and will proactively seek assistance from experts.

Our work will deliver a step change in developing resilient autonomous systems and systems of systems. Developers will have notations and guidance to specify the socio-technical norms and rules applicable to the operational context of their autonomous systems, and techniques to design resilient autonomous systems that are trustworthy and compliant with these norms and rules. Additionally, developers will have guidance to build autonomous systems that can tolerate disruption, making the system usable in a larger set of circumstances. Finally, they will have techniques to develop resilient autonomous systems that can share information and services with peer systems and humans, and methods for providing evidence of the resilience of their systems. In such a context, autonomous systems and systems of systems will be highly resilient and trustworthy.

Persons:Resilience Node Team Members

ENsurance of Software evolUtion by Run-time cErtification

Software is an innovation driver in many different domains, e.g, 90% of the innovation in cars is realized by software. Hence, the quality of the software is of utmost importance and needs to be properly addressed during evolution. Examples of quality attributes which ENSURE-II addresses are safety in embedded systems and performance in business information systems. Currently, the quality is usually analyzed at design time under non-perfect knowledge about the behavior of the system and its environment which can result in incorrect analysis results.

Hence, ENSURE-II addresses this problem by a holistic model-driven approach, which treats quality evaluation models as first class entities. We focus on probabilistic quality properties, e.g., reliability, availability and safety. In the first phase, we developed a co-evolution approach for architectural as well as quality evaluation models which supports incremental change propagation between the models. This is complemented by an approach to efficiently learn the attributes of the quality evaluation models from the actual running system and an approach to specify the quality properties to analyze using controlled natural language. Complementary to these activities, we empirically studied model-driven engineering and its challenges related to our topics as well as how meta models of modeling languages evolve. We participated in both demonstrators, focussing on the Pick&Place Unit (PPU), and evaluated our approach on the PPU case study

In the second phase, while addressing all three guiding themes of the SPP, we will focus more on the guiding theme of platforms and environments for evolution. We will specifically extend our co-evolution approach by providing recommendation support for cases where the co-evolution specifications do not provide deterministic co-evolution using machines learning techniques on model histories. The second major extension is exploiting the information from the model changes from the co-evolution for performance improvement of the quality analysis by an incremental approach. Finally, we will empirically study and evaluate the results from both phases with experts from industry as well as both demonstrators of the SPP. We will continue to be well integrated in the activities of the SPP.

Persons:

A Domain Specific Modeling Language for Semantic Web enabled Multi-agent Systems

Software agents are considered to be autonomous entities which contain intelligence that serves for solving their selfish or common problems, and to achieve certain goals. These agents constitute Multi-agent Systems (MAS). However, the autonomous, responsive, and proactive natures of agents make the development of agent-based software systems more complex than other software systems. Furthermore, the design and implementation of a MAS may become even more complex and difficult to implement when considering new requirements and interactions for new agent environments like the Semantic Web. Both domain-specific modeling and the use of a domain-specific modeling language (DSML) may provide the required abstraction, and hence support a more fruitful methodology for the development of MASs. Within this context, a DSML has been developed for the design and implementation of MAS with including all of its components and supporting software tools in this project. In addition to the classical viewpoints of a MAS, the proposed DSML includes new viewpoints which specifically support the development of software agents working in the Semantic Web environment. At first, a metamodel and an abstract syntax were defined for the DSML. Later, both graphical and textual concrete syntaxes were developed. Upon completion of the formal definition of the semantics, operational semantics was derived via model transformations in order to provide the real implementation of the designed MAS models. Codes for the agent software can be automatically achieved as the result of applying model to code transformations. All required tools for MAS modeling and developing software according to the DSML were also constructed in this project.

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