Design and Optimisation of the FlyFast Front-end for Attribute-based Coordination

TL;DR

This paper introduces PiFF, a modeling language for FlyFast, and details a state-space reduction method using probabilistic bisimulation to improve analysis of large-scale collective adaptive systems.

Contribution

It presents the design of PiFF and a probabilistic bisimulation approach for state-space reduction in FlyFast's analysis of large systems.

Findings

PiFF enables effective modeling of collective adaptive systems.

Probabilistic bisimulation reduces state space significantly.

Enhanced analysis scalability for large populations.

Abstract

Collective Adaptive Systems (CAS) consist of a large number of interacting objects. The design of such systems requires scalable analysis tools and methods, which have necessarily to rely on some form of approximation of the system's actual behaviour. Promising techniques are those based on mean-field approximation. The FlyFast model-checker uses an on-the-fly algorithm for bounded PCTL model-checking of selected individual(s) in the context of very large populations whose global behaviour is approximated using deterministic limit mean-field techniques. Recently, a front-end for FlyFast has been proposed which provides a modelling language, PiFF in the sequel, for the Predicate-based Interaction for FlyFast. In this paper we present details of PiFF design and an approach to state-space reduction based on probabilistic bisimulation for inhomogeneous DTMCs.