A logic for model-checking mean-field models

Kolesnichenko, Anna; Boer, Pieter-Tjerk de; Remke, Anne; Haverkort, Boudewijn R.

doi:10.1109/dsn.2013.6575345

Cited by 16 publications

(36 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…-moving beyond CSL to consider more complex path properties, for instance those expressed by Deterministic Timed Automata [19] (DTA), obtaining a logic for individual properties similar to asCSL [6] and CSL-TA [23]; -the lifting of individual specifications to the collective level, similarly to [32].…”

Section: Discussionmentioning

confidence: 99%

“…When at steady state, the rates of z(t) do not depend on time anymore, hence it becomes a time-homogeneous CTMC. Therefore, to model check a formula like S (ϕ), we just need to model check ϕ against this time-homogeneous CTMC, with standard algorithms, and then compute the satisfaction of the steady state operator as in the standard model checking for CTMC [5] (see also [32]). …”

Section: Nested Csl Formulaementioning

confidence: 99%

See 1 more Smart Citation

Checking Individual Agent Behaviours in Markov Population Models by Fluid Approximation

Bortolussi

Hillston

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In this chapter, we will describe, in a tutorial style, recent work on the use of fluid approximation techniques in the context of stochastic model checking. We will discuss the theoretical background and the algorithms working out an example. This approach is designed for population models, in which a (large) number of individual agents interact, which give rise to continuous time Markov chain (CTMC) models with a very large state space. We then focus on properties of individual agents in the system, specified by Continuous Stochastic Logic (CSL) formulae, and use fluid approximation techniques (specifically, the so called fast simulation) to check those properties. We will show that verification of such CSL formulae reduces to the computation of reachability probabilities in a special kind of timeinhomogeneous CTMC with a small state space, in which both the rates and the structure of the CTMC can change (discontinuously) with time. In this tutorial, we will discuss only briefly the theoretical issues behind the approach, like the decidability of the method and the consistency of the approximation scheme.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Nested Csl Formulaementioning

confidence: 99%

Checking Individual Agent Behaviours in Markov Population Models by Fluid Approximation

Bortolussi

Hillston

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Preliminary ideas on using fluid approximation in continuous time for model checking population models, and in particular for an extension of the logic CSL, were informally sketched in [43], but no model checking algorithms were presented. Subsequently the work was more fully developed in [44], which relies substantially on [11].…”

Section: Fluid Model Checkingmentioning

confidence: 99%

The Benefits of Sometimes Not Being Discrete

Hillston

2014

CONCUR 2014 – Concurrency Theory

View full text Add to dashboard Cite

Abstract. Discrete representations of systems are usual in theoretical computer science and they have many benefits. Unfortunately they also suffer from the problem of state space explosion, sometimes termed the curse of dimensionality. In recent years, research has shown that there are cases in which we can reap the benefits of discrete representation during system description but then gain from more efficient analysis by approximating the discrete system by a continuous one. This paper will motivate this approach, explaining the theoretical foundations and their practical benefits.

show abstract

“…Next to the fluid model checking the reader might be interested in the techniques for calculation fluid passage time, as discussed in [24] and an MF-CSL logic, which allows checking properties of the overall mean-field model via properties of the individual object [32].…”

Section: Examplesmentioning

confidence: 99%

Applying Mean-Field Approximation to Continuous Time Markov Chains

Kolesnichenko

Senni

Pourranjabar

et al. 2014

Stochastic Model Checking. Rigorous Dependability Analysis Using Model Checking Techniques for Stochastic Systems

Self Cite

View full text Add to dashboard Cite

Abstract. The mean-field analysis technique is used to perform analysis of a systems with a large number of components to determine the emergent deterministic behaviour and how this behaviour modifies when its parameters are perturbed. The computer science performance modelling and analysis community has found the mean-field method useful for modelling large-scale computer and communication networks. Applying mean-field analysis from the computer science perspective requires the following major steps: (1) describing how the agents populations evolve by means of a system of differential equations, (2) finding the emergent deterministic behaviour of the system by solving such differential equations, and (3) analysing properties of this behaviour either by relying on simulation or by using logics. Depending on the system under analysis, performing these steps may become challenging. Often, modifications of the general idea are needed. In this tutorial we consider illustrating examples to discuss how the mean-field method is used in different application areas. Starting from the application of the classical technique, moving to cases where additional steps have to be used, such as systems with local communication. Finally we illustrate the application of the simulation and fluid model checking analysis techniques.

show abstract

A logic for model-checking mean-field models

Cited by 16 publications

References 14 publications

Checking Individual Agent Behaviours in Markov Population Models by Fluid Approximation

Checking Individual Agent Behaviours in Markov Population Models by Fluid Approximation

The Benefits of Sometimes Not Being Discrete

Applying Mean-Field Approximation to Continuous Time Markov Chains

Contact Info

Product

Resources

About