Distributed Priority Synthesis

TL;DR

This paper introduces a formal approach for synthesizing distributed priorities in component-based systems to ensure safety, utilizing game-theoretic analysis, optimizations, and SAT solving, validated through case studies.

Contribution

It presents a novel formal framework for distributed priority synthesis using multi-player safety games, with optimized algorithms integrated into the VissBIP tool.

Findings

Effective algorithms for distributed priority synthesis

Successful validation on scheduling and robotics case studies

Enhanced scalability through diagnosis and SAT-based optimizations

Abstract

Given a set of interacting components with non-deterministic variable update and given safety requirements, the goal of priority synthesis is to restrict, by means of priorities, the set of possible interactions in such a way as to guarantee the given safety conditions for all possible runs. In distributed priority synthesis we are interested in obtaining local sets of priorities, which are deployed in terms of local component controllers sharing intended next moves between components in local neighborhoods only. These possible communication paths between local controllers are specified by means of a communication architecture. We formally define the problem of distributed priority synthesis in terms of a multi-player safety game between players for (angelically) selecting the next transition of the components and an environment for (demonically) updating uncontrollable variables. We analyze the complexity of the problem, and propose several optimizations including a solution-space exploration based on a diagnosis method using a nested extension of the usual attractor computation in games together with a reduction to corresponding SAT problems. When diagnosis fails, the method proposes potential candidates to guide the exploration. These optimized algorithms for solving distributed priority synthesis problems have been integrated into the VissBIP framework. An experimental validation of this implementation is performed using a range of case studies including scheduling in multicore processors and modular robotics.