Formal Synthesis of Uncertainty Reduction Controllers

Marc Carwehl*, Calum Corrie Imrie, Thomas Vogel, Genaina Rodrigues, Radu Calinescu, Lars Grunske

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In its quest for approaches to taming uncertainty in self-adaptive systems (SAS), the research community has largely focused on solutions that adapt the SAS architecture or behaviour in response to uncertainty. By comparison, solutions that reduce the uncertainty affecting SAS (other than through the blanket monitoring of their components and environment) remain underexplored. Our paper proposes a more nuanced, adaptive approach to SAS uncertainty reduction. To that end, we introduce a SAS architecture comprising an uncertainty reduction controller that drives the adaptive acquisition of new information within the SAS adaptation loop, and a tool-supported method that uses probabilistic model checking to synthesise such controllers. The controllers generated by our method deliver optimal trade-offs between SAS uncertainty reduction benefits and new information acquisition costs. We illustrate the use and evaluate the effectiveness of our approach for mobile robot navigation and server infrastructure management SAS.
Original languageEnglish
Title of host publication19th International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2024)
PublisherACM
Publication statusAccepted/In press - 13 Jan 2024
Event19th International Conference on Software Engineering for Adaptive and Self-Managing Systems - Lisbon, Portugal
Duration: 15 Apr 202416 Apr 2024

Conference

Conference19th International Conference on Software Engineering for Adaptive and Self-Managing Systems
Abbreviated titleSEAMS 2024
Country/TerritoryPortugal
CityLisbon
Period15/04/2416/04/24

Bibliographical note

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