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From Verification to Causality-Based Explications (Invited Talk)

Authors Christel Baier , Clemens Dubslaff , Florian Funke , Simon Jantsch , Rupak Majumdar , Jakob Piribauer , Robin Ziemek



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Christel Baier
  • Technische Universität Dresden, Germany
Clemens Dubslaff
  • Technische Universität Dresden, Germany
Florian Funke
  • Technische Universität Dresden, Germany
Simon Jantsch
  • Technische Universität Dresden, Germany
Rupak Majumdar
  • MPI-SWS, Kaiserslautern, Germany
Jakob Piribauer
  • Technische Universität Dresden, Germany
Robin Ziemek
  • Technische Universität Dresden, Germany

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Christel Baier, Clemens Dubslaff, Florian Funke, Simon Jantsch, Rupak Majumdar, Jakob Piribauer, and Robin Ziemek. From Verification to Causality-Based Explications (Invited Talk). In 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 198, pp. 1:1-1:20, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.ICALP.2021.1

Abstract

In view of the growing complexity of modern software architectures, formal models are increasingly used to understand why a system works the way it does, opposed to simply verifying that it behaves as intended. This paper surveys approaches to formally explicate the observable behavior of reactive systems. We describe how Halpern and Pearl’s notion of actual causation inspired verification-oriented studies of cause-effect relationships in the evolution of a system. A second focus lies on applications of the Shapley value to responsibility ascriptions, aimed to measure the influence of an event on an observable effect. Finally, formal approaches to probabilistic causation are collected and connected, and their relevance to the understanding of probabilistic systems is discussed.

Subject Classification

ACM Subject Classification
  • Theory of computation → Logic and verification
Keywords
  • Model Checking
  • Causality
  • Responsibility
  • Counterfactuals
  • Shapley value

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