Incorporating Monitors in Reactive Synthesis without Paying the Price
Paper i proceeding, 2021

Temporal synthesis attempts to construct reactive programs that satisfy a given declarative (LTL) formula. Practitioners have found it challenging to work exclusively with declarative specifications, and have found languages that combine modelling with declarative specifications more useful. Synthesised controllers may also need to work with pre-existing or manually constructed programs. In this paper we explore an approach that combines synthesis of declarative specifications in the presence of an existing behaviour model as a monitor, with the benefit of not having to reason about the state space of the monitor. We suggest a formal language with automata monitors as non-repeating and repeating triggers for LTL formulas. We use symbolic automata with memory as triggers, resulting in a strictly more expressive and succinct language than existing regular expression triggers. We give a compositional synthesis procedure for this language, where reasoning about the monitor state space is minimal. To show the advantages of our approach we apply it to specifications requiring counting and constraints over arbitrarily long sequence of events, where we can also see the power of parametrisation, easily handled in our approach. We provide a tool to construct controllers (in the form of symbolic automata) for our language.

Författare

Shaun Azzopardi

Chalmers, Data- och informationsteknik, Formella metoder

Göteborgs universitet

Nir Piterman

Chalmers, Data- och informationsteknik, Formella metoder

Göteborgs universitet

Gerardo Schneider

Data Science och AI

Göteborgs universitet

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 12971 LNCS 337-353
9783030888848 (ISBN)

19th International Symposium on Automated Technology for Verification and Analysis, ATVA 2021
Virtual, Gold Coast, Australia,

Ämneskategorier (SSIF 2025)

Datavetenskap (datalogi)

DOI

10.1007/978-3-030-88885-5_22

Mer information

Senast uppdaterat

2025-06-30