Good-Enough Synthesis

Shaull Almagor*, Orna Kupferman

*Corresponding author for this work

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

10 Scopus citations


We introduce and study good-enough synthesis (ge-synthesis) – a variant of synthesis in which the system is required to satisfy a given specification only when it interacts with an environments for which a satisfying interaction exists. Formally, an input sequence x is hopeful if there exists some output sequence y such that the induced computation satisfies, and a system ge-realizes if it generates a computation that satisfies on all hopeful input sequences. ge-synthesis is particularly relevant when the notion of correctness is multi-valued (rather than Boolean), and thus we seek systems of the highest possible quality, and when synthesizing autonomous systems, which interact with unexpected environments and are often only expected to do their best. We study ge-synthesis in Boolean and multi-valued settings. In both, we suggest and solve various definitions of ge-synthesis, corresponding to different ways a designer may want to take hopefulness into account. We show that in all variants, ge-synthesis is not computationally harder than traditional synthesis, and can be implemented on top of existing tools. Our algorithms are based on careful combinations of nondeterministic and universal automata. We augment systems that ge-realize their specifications by monitors that provide satisfaction information. In the multi-valued setting, we provide both a worst-case analysis and an expectation-based one, the latter corresponding to an interaction with a stochastic environment.

Original languageAmerican English
Title of host publicationComputer Aided Verification - 32nd International Conference, CAV 2020, Proceedings
EditorsShuvendu K. Lahiri, Chao Wang
Number of pages23
ISBN (Print)9783030532901
StatePublished - 2020
Event32nd International Conference on Computer Aided Verification, CAV 2020 - Los Angeles, United States
Duration: 21 Jul 202024 Jul 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12225 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference32nd International Conference on Computer Aided Verification, CAV 2020
Country/TerritoryUnited States
CityLos Angeles

Bibliographical note

Publisher Copyright:
© 2020, The Author(s).


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