Apr 10

Computer Science Seminar: Niklas Metzger (CISPA Helmholtz Center for Information Security)

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Milstein 516 and Zoom
  • Add to Calendar 2023-04-10 14:00:00 2023-04-10 15:00:00 Computer Science Seminar: Niklas Metzger (CISPA Helmholtz Center for Information Security) Speaker: Niklas Metzger (CISPA Helmholtz Center for Information Security) Title: Actual Causality in Reactive Systems The seminar will be available for in-person and Zoom participation. If you would like to receive the Zoom link, please e-mail Prof. Mark Santolucito at msantolu@barnard.edu. Counterfactual reasoning is an approach to infer the cause of an observed effect by comparing a given scenario in which the suspected cause and the effect are present, to the hypothetical scenarios where the suspected cause is not present. The seminal works of Halpern and Pearl have provided a definition of counterfactual causality for finite settings. In this talk, we propose an approach to check causality for reactive systems, i.e., systems that interact with their environment over a possibly infinite duration. First, we focus on finding causes for violations of hyperproperties. Hyperproperties, unlike trace properties, can relate multiple traces and thus express complex security properties. Here, the suspected cause is represented by a finite set of events occurring on the set of traces. Then, we lift Halpern and Pearl's definition to the case where the causes themselves (as well as effects) are omega-regular properties, not just sets of events. Given the causality algorithms, our tool HyperVis generates interactive visualizations of the given model, specification, and cause of the counterexample. Niklas Metzger is a PhD student at CISPA Helmholtz Center for Information Security in Germany. He is advised by Bernd Finkbeiner and a member of the Reactive Systems Group. Before joining CISPA, he received his BSc and MSc at Saarland University in Germany. Niklas’ research focuses on compositional reactive synthesis guided by the principles of knowledge, actual causality in reactive systems, and building machine learning models as heuristics in complex formal method tasks. Milstein 516 and Zoom Barnard College barnard-admin@digitalpulp.com America/New_York public

Speaker: Niklas Metzger (CISPA Helmholtz Center for Information Security)
Title: 
Actual Causality in Reactive Systems

The seminar will be available for in-person and Zoom participation. If you would like to receive the Zoom link, please e-mail Prof. Mark Santolucito at msantolu@barnard.edu.

Counterfactual reasoning is an approach to infer the cause of an observed effect by comparing a given scenario in which the suspected cause and the effect are present, to the hypothetical scenarios where the suspected cause is not present. The seminal works of Halpern and Pearl have provided a definition of counterfactual causality for finite settings. In this talk, we propose an approach to check causality for reactive systems, i.e., systems that interact with their environment over a possibly infinite duration. First, we focus on finding causes for violations of hyperproperties. Hyperproperties, unlike trace properties, can relate multiple traces and thus express complex security properties. Here, the suspected cause is represented by a finite set of events occurring on the set of traces. Then, we lift Halpern and Pearl's definition to the case where the causes themselves (as well as effects) are omega-regular properties, not just sets of events. Given the causality algorithms, our tool HyperVis generates interactive visualizations of the given model, specification, and cause of the counterexample.


Niklas Metzger is a PhD student at CISPA Helmholtz Center for Information Security in Germany. He is advised by Bernd Finkbeiner and a member of the Reactive Systems Group. Before joining CISPA, he received his BSc and MSc at Saarland University in Germany. Niklas’ research focuses on compositional reactive synthesis guided by the principles of knowledge, actual causality in reactive systems, and building machine learning models as heuristics in complex formal method tasks.