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.

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.

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- y 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.

Typical Collective Adaptive Systems (CAS) consist of a large number of interacting objects that coordinate their activities in a decentralised and often implicit way. The design of such systems is challenging, as it requires scalable analysis tools and methods to check properties of proposed system designs before they are put into operation. A promising technique is Fast Mean-Field Approximated Model-checking. The FlyFast model-checker uses an on-the-fly algorithm for bounded PCTL model-checking of selected individuals in the context of very large populations whose global behaviour is approximated using deterministic limit techniques. Recently, specific modelling languages have been proposed for CAS. A key feature of such languages is the attribute-based interaction paradigm. In this paper we present an attribute-based coordination language as a front-end for FlyFast. Its formal probabilistic semantics is provided and a translation to the original FlyFast language is given and proved correct. Application examples are also provided.

Typical Collective Adaptive Systems (CAS) consist of a large number of interacting objects that coordinate their activities in a decentralised and often implicit way. The design of such systems is challenging. It requires scalable analysis tools and techniques to check properties of proposed system designs before they are put into operation. A promising technique is Fast Mean- Field Approximated Model-checking. In particular, the FlyFast model-checker uses an on-the-fly algorithm for bounded PCTL model-checking of selected individuals in the context of very large populations whose global behaviour is approximated using deterministic limit techniques. Recently, specific modelling languages have been proposed for CAS. A key feature of such languages is the attribute-based interaction paradigm. In this paper we present a simple non value-passing version of an attribute-based coordination language as a front-end for FlyFast. Its formal probabilistic semantics is provided and a translation to the original FlyFast language is presented and proved correct. Application examples are also provided.

TypicalCollectiveAdaptiveSystems(CAS)consistofalarge number of interacting objects that coordinate their activities in a decen- tralised and often implicit way. The design of such systems is challenging. It requires scalable analysis tools and techniques to check properties of proposed system designs before they are put into operation. A promising technique is Fast Mean-Field Approximated Model-checking. In particu- lar, the FlyFast model-checker uses an on-the-fly algorithm for bounded PCTL model-checking of selected individuals in the context of very large populations whose behaviour is approximated using deterministic limit techniques. Recently, specific modelling languages have been proposed for CAS. A key feature of such languages is the attribute-based interac- tion paradigm. In this paper we present an attribute-based coordination language as a front-end for FlyFast. Its formal probabilistic semantics is provided and a translation to the original FlyFast language is presented and proved correct.

We show that, under suitable convergence and scaling conditions, fluid model checking bounded CSL formulas on selected individuals in a continuous large population model can be approximated by checking equivalent bounded PCTL formulas on corresponding objects in a discrete time, time synchronous Markov population model, using an on-the-fly mean field approach. The proposed technique is applied to a benchmark epidemic model and a client-server case study showing promising results also for the challenging case of nested formulas with time dependent truth values. The on-the-fly results are compared to those obtained via global fluid model checking and statistical model-checking.

Typical self-organising collective systems consist of a large number of interacting objects that coordinate their activities in a decentralised and often implicit way. Design of such systems is challenging and requires suitable, scalable analysis tools to check properties of proposed system designs before they are put into operation. We present a novel scalable, on-the-fly approximated model-checking procedure to verify bounded PCTL properties of selected individuals in the context of very large systems of independent interacting objects. The proposed procedure combines on-the-fly model-checking techniques with deterministic mean-field approximation in discrete time. The asymptotic correctness of the procedure is proven and a prototype implementation of the model-checker is presented. The potential of the verification approach is illustrated by its application on self-organising collective systems and an overview of remaining open issues and future extensions is provided.

Verification is the process of assessing how well a system meets a specification or requirement. A variety of approaches have appeared in the literature, ranging from model checking to static analysis of source code and theorem proving. In this working group, we primarily focused on verification based on model checking, in which a state-based description of the system is assessed with respect to a property expressed in an appropriate specification language, like a temporal logic. In particular, we considered the challenges that arise in the model checking of Complex Adaptive Systems (CAS), which are systems comprised of a large number of heterogeneous agents which, interacting together, produce a complex array of collective behaviours. In our working group in Dagstuhl, we first identified the major issues and more interesting challenges that arise in the process of verification of CAS. Afterwards, we divided in two subgroups to discuss in detail specific topics related to this general framework. In particular, we chose to investigate: (1) the quantitative or stochastic model checking in the presence of uncertainty, and (2) the specifications and logics which capture the spatial arrangements of systems, characterising the impact of those arrangements on collective behaviour. A brief account of each subgroup is given below.

In this abstract work on the application of mean-field theory in the definition and implementation of on-the-fly probabilistic model checking of CAS.