The Modest State of Learning, Sampling, and Verifying Strategies

Arnd Hartmanns, Michaela Klauck

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

1 Citation (Scopus)
25 Downloads (Pure)

Abstract

Optimal decision-making under stochastic uncertainty is a core problem tackled in artificial intelligence/machine learning (AI), planning, and verification. Planning and AI methods aim to find good or optimal strategies to maximise rewards or the probability of reaching a goal. Verification approaches focus on calculating the probability or reward, obtaining the strategy as a side effect. In this paper, we connect three strands of work on obtaining strategies implemented in the context of the Modest Toolset: statistical model checking with either lightweight scheduler sampling or deep learning, and probabilistic model checking. We compare their different goals and abilities, and show newly extended experiments on Racetrack benchmarks that highlight the tradeoffs between the methods. We conclude with an outlook on improving the existing approaches and on generalisations to continuous models, and emphasise the need for further tool development to integrate methods that find, evaluate, compare, and explain strategies.
Original languageEnglish
Title of host publicationLeveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning
Subtitle of host publication11th International Symposium, ISoLA 2022, Rhodes, Greece, October 22-30, 2022, Proceedings, Part III
EditorsTiziana Margaria, Bernhard Steffen
Place of PublicationCham
PublisherSpringer
Pages406-432
Number of pages27
VolumePart III
ISBN (Electronic)978-3-031-19759-8
ISBN (Print)978-3-031-19758-1
DOIs
Publication statusPublished - 17 Oct 2022

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13703
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • This work was part of the MISSION (Models in Space Systems: Integration, Operation, and Networking) project, funded by the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie Actions grant number 101008233.
  • 2023 OA procedure

Fingerprint

Dive into the research topics of 'The Modest State of Learning, Sampling, and Verifying Strategies'. Together they form a unique fingerprint.

Cite this