Good-Enough Synthesis

TL;DR

This paper introduces good-enough synthesis (GE-synthesis), a relaxed approach to system synthesis that ensures correctness only for hopeful input sequences, making it more practical for autonomous systems interacting with unpredictable environments.

Contribution

The paper proposes and solves new definitions of GE-synthesis in Boolean and multi-valued settings, showing it is computationally comparable to traditional synthesis and implementable with existing tools.

Findings

GE-synthesis is not harder than traditional synthesis.

Algorithms are based on nondeterministic and universal automata.

Provides both worst-case and expectation-based analysis in multi-valued setting.

Abstract

In the classical synthesis problem, we are given an LTL formula \psi over sets of input and output signals, and we synthesize a system T that realizes \psi: with every input sequences x, the system associates an output sequence T(x) such that the generated computation x \otimes T(x) satisfies \psi. In practice, the requirement to satisfy the specification in all environments is often too strong, and it is common to add assumptions on the environment. We introduce a new type of relaxation on this requirement. In good-enough synthesis (GE-synthesis), the system is required to generate a satisfying computation only if one exists. Formally, an input sequence x is hopeful if there exists some output sequence y such that the computation x \otimes y satisfies \psi, and a system GE-realizes \psi if it generates a computation that satisfies \psi 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.