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From verification to causality-based explications

  • Christel Baier
  • , Clemens Dubslaff
  • , Florian Funke
  • , Simon Jantsch
  • , Rupak Majumdar
  • , Jakob Piribauer
  • , Robin Ziemek
  • Technische Universität Dresden
  • Max Planck Institute for Software Systems

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Scopus citations

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 verificationoriented 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.

Original languageEnglish
Title of host publication48th International Colloquium on Automata, Languages, and Programming, ICALP 2021
EditorsNikhil Bansal, Emanuela Merelli, James Worrell
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959771955
DOIs
StatePublished - 1 Jul 2021
Externally publishedYes
Event48th International Colloquium on Automata, Languages, and Programming, ICALP 2021 - Virtual, Glasgow, United Kingdom
Duration: 12 Jul 202116 Jul 2021

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume198
ISSN (Print)1868-8969

Conference

Conference48th International Colloquium on Automata, Languages, and Programming, ICALP 2021
Country/TerritoryUnited Kingdom
CityVirtual, Glasgow
Period12/07/2116/07/21

Keywords

  • Causality
  • Counterfactuals
  • Model Checking
  • Responsibility
  • Shapley value

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