Learning Symbolic Timed Models from Concrete Timed Data

Simon Dierl, Falk Maria Howar, Sean Kauffman*, Martin Kristjansen, Kim Guldstrand Larsen, Florian Lorber, Malte Mauritz

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)
23 Downloads (Pure)

Abstract

We present a technique for learning explainable timed automata from passive observations of a black-box function, such as an artificial intelligence system. Our method accepts a single, long, timed word with mixed input and output actions and learns a Mealy machine with one timer. The primary advantage of our approach is that it constructs a symbolic observation tree from a concrete timed word. This symbolic tree is then transformed into a human comprehensible automaton. We provide a prototype implementation and evaluate it by learning the controllers of two systems: a brick-sorter conveyor belt trained with reinforcement learning and a real-world derived smart traffic light controller. We compare different model generators using our symbolic observation tree as their input and achieve the best results using k-tails. In our experiments, we learn smaller and simpler automata than existing passive timed learners while maintaining accuracy.

Original languageEnglish
Title of host publicationNASA Formal Methods : 15th International Symposium, NFM 2023, Houston, TX, USA, May 16–18, 2023, Proceedings
EditorsKristin Yvonne Rozier, Swarat Chaudhuri
Number of pages18
PublisherSpringer Science+Business Media
Publication date16 May 2023
Pages104-121
ISBN (Print)978-3-031-33169-5
ISBN (Electronic)978-3-031-33170-1
DOIs
Publication statusPublished - 16 May 2023
Event15th International Symposium on NASA Formal Methods, NFM 2023 - Houston, United States
Duration: 16 May 202318 May 2023

Conference

Conference15th International Symposium on NASA Formal Methods, NFM 2023
Country/TerritoryUnited States
CityHouston
Period16/05/202318/05/2023
SeriesLecture Notes in Computer Science (LNCS)
Volume13903 LNCS
ISSN0302-9743

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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