Autonomous systems are developed at a staggering pace for use in healthcare, transportation, manufacturing, and many other domains. Often comprising artificial intelligence (AI) components, these systems are uniquely capable of performing tasks and making decisions in changing environments, and can operate without human intervention for extended periods of time. As such, autonomous systems have the potential to undertake or support complex missions that are dangerous, difficult or tedious for humans.

To achieve this potential, autonomous systems must be resilient, i.e., they must continue to provide the required functionality despite the uncertainty, change, faults, failure, adversity, and other (anticipated and unforeseen) disruptions within their operating environments. Numerous methods for developing resilient autonomous systems have been proposed, including: (1) design methods for developing autonomous systems that resist disruption through robustness and fault tolerance; (2) reactive adaptation methods which ensure that autonomous systems absorb disruption, e.g., by graceful degradation; and (3) proactive adaptation methods that anticipate disruption by recognizing patterns of evolution, and avoiding disruption proactively. Additional research has been devoted to ensuring that these methods can be used without compromising the safety or violating the ethical norms and rules of system users and operators.

The ability of autonomous systems to achieve their goals in open real-world environments can be further increased by making them antifragile. Antifragile systems benefit from exposure to uncertainty and disruption, by learning from encounters with such difficulties, so that they can handle their future occurrences faster, more efficiently, with lower user impact, etc. The inspiration for antifragility comes from nature, where antifragile systems are ubiquitous. For example, the immune system responds to exposure to pathogens by producing antibodies that help protect against future infections.

Despite recent advances in autonomous technologies, the research on resilient autonomous systems remains fragmented and lacks industrial adoption, and that on antifragile autonomous systems is in its infancy. As these closely related research areas play a key role in the realization of the societal and economic benefits of autonomous systems, now is the right time for the international research communities from these areas to come together, to identify synergies across their disciplines and research programmes, and to agree on a common basis for joint future research.

This Dagstuhl Seminar aims to unify the international research on resilient and antifragile autonomous systems (RAAS), leading to faster scientific advancements and industrial adoption. To that end, the seminar wants to bring together leading researchers and practitioners with expertise in autonomous system resilience, antifragility, safety, and ethics from disciplines including Computer Science, Computational Biology, and Ethics, to share and discuss each other’s understanding of, methods for, and open challenges related to RAAS. These participants will work closely together to: (1) survey the current RAAS research in order to develop and document a common understanding of the RAAS research landscape; (2) identify RAAS open challenges and promising preliminary approaches to tackling them; (3) set an international research agenda for addressing these challenges; (4) define a roadmap for the delivery of this agenda; and (5) agree on use cases (e.g., from health and assistive care, transportation, aviation and aerospace) that can be used as a benchmark for the evaluation of future RAAS solutions.

The seminar and its outputs will cover a broad range of RAAS topics. These topics will include: (1) RAAS concepts, terminology and measures; (2) state-of-the-art methods for autonomous system resilience and their integration; (3) nature-inspired approaches to autonomous system resilience and antifragility; and (4) safety and ethical concerns of RAAS.

Creative Commons BY 4.0

Simon Burton, Radu Calinescu, Raffaela Mirandola, and Corina Pasareanu