Distributed Priority Synthesis and its Applications

TL;DR

This paper introduces a formal approach to distributed priority synthesis for safety guarantees in interacting components, with optimized algorithms implemented in the VissBIP framework and validated on case studies.

Contribution

It formalizes distributed priority synthesis as a multi-player safety game, proposes optimization techniques, and integrates them into a practical framework with experimental validation.

Findings

Optimized algorithms effectively solve distributed priority synthesis problems.

The approach guarantees safety in complex multi-component systems.

Experimental results demonstrate applicability to real-world scenarios.

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; this problem is NP-complete. We 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 our VissBIP framework. An experimental validation of this implementation is performed using a range of case studies including scheduling in multicore processors and modular robotics.