Good-Enough Synthesis

Abstract

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.