Resilience and Antifragility of Autonomous Systems (Dagstuhl Seminar 24182)

Authors Simon Burton, Radu Calinescu, Raffaela Mirandola and all authors of the abstracts in this report



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Author Details

Simon Burton
  • University of York, GB
Radu Calinescu
  • University of York, GB
Raffaela Mirandola
  • Karlsruhe Institute of Technology, KIT, DE
and all authors of the abstracts in this report

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Simon Burton, Radu Calinescu, and Raffaela Mirandola. Resilience and Antifragility of Autonomous Systems (Dagstuhl Seminar 24182). In Dagstuhl Reports, Volume 14, Issue 4, pp. 142-163, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/DagRep.14.4.142

Abstract

In healthcare, transportation, manufacturing, and many other domains, autonomous systems have the potential to undertake or support complex missions that are dangerous, difficult, or tedious for humans. However, to achieve this potential, autonomous systems must be resilient: they must continue to provide the required functionality despite the anticipated and unforeseen disturbances encountered within their operating environments. This ability to achieve user goals in open-world environments can be further increased by making autonomous systems antifragile. Antifragile systems benefit from exposure to uncertainty and disturbances, by learning from encounters with such difficulties, so that they can handle their future occurrences faster, more efficiently, with lower user impact, etc. This Dagstuhl Seminar brought together leading researchers and practitioners with expertise in autonomous system resilience, antifragility, safety and ethics, self-adaptive systems, and formal methods, with the aim to: (1) develop and document a common understanding of resilient and antifragile autonomous systems (RAAS); (2) identify open challenges for RAAS; (3) discuss promising preliminary approaches; and (4) propose a research agenda for addressing these challenges.

Subject Classification

ACM Subject Classification
  • General and reference → Reliability
  • General and reference → Metrics
  • General and reference → Validation
  • Computer systems organization → Embedded and cyber-physical systems
  • Computer systems organization → Dependable and fault-tolerant systems and networks
  • Software and its engineering
  • Theory of computation → Logic
  • Mathematics of computing → Probability and statistics
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Machine learning
  • Human-centered computing
Keywords
  • artificial intelligence
  • antifragility
  • autonomous systems
  • disturbance
  • ethics
  • formal methods
  • machine learning
  • nondeterminism
  • resilience
  • safety
  • self-adaptive systems
  • validation and verification
  • uncertainty

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