Challenges for Quantitative Analysis of Collective Adaptive Systems

Hillston, Jane

doi:10.1007/978-3-319-05119-2_2

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…SCEL (De Nicola et al, 2014b is a formal language for the description and verification of collective adaptive systems (Hillston, 2014). In order to capture the highly dynamic nature of this class of systems, it naturally supports concepts such as open-endedness and anonymity.…”

Section: Scel (Software Component Ensemble Language)mentioning

confidence: 99%

Toward Formal Models and Languages for Verifiable Multi-Robot Systems

2018

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Incorrect operations of a Multi-Robot System (MRS) may not only lead to unsatisfactory results, but can also cause economic losses and threats to safety. These threats may not always be apparent, since they may arise as unforeseen consequences of the interactions between elements of the system. This call for tools and techniques that can help in providing guarantees about MRSs behaviour. We think that, whenever possible, these guarantees should be backed up by formal proofs to complement traditional approaches based on testing and simulation.We believe that tailored linguistic support to specify MRSs is a major step towards this goal. In particular, reducing the gap between typical features of an MRS and the level of abstraction of the linguistic primitives would simplify both the specification of these systems and the verification of their properties. In this work, we review different agent-oriented languages and their features; we then consider a selection of case studies of interest and implement them useing the surveyed languages. We also evaluate and compare effectiveness of the proposed solution, considering, in particular, easiness of expressing non-trivial behaviour.

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Section: Scel (Software Component Ensemble Language)mentioning

confidence: 99%

Toward Formal Models and Languages for Verifiable Multi-Robot Systems

2018

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“…Population models are widely used to model different phenomena: animal collectives such as social insects, flocking birds, schooling fish, or humans within societies, as well as molecular species inside a cell, or cells forming a tissue. Quantitative models of the underlying mechanisms can directly serve important societal actions such as disaster response (for example, mitigating the spread of epidemics [ 1 ]), they can inspire the design of distributed algorithms (for example, ant colony algorithm [ 2 ]), or aid robust design and engineering of collective, adaptive systems under given functionality and resources, which is recently gaining attention in vision of smart cities [ 3 , 4 ]. In practice, the qualitative aspects of population models—the existence of connections between different population states—are usually easy to hypothesise, as they can be inferred from the local interaction mechanisms between individual agents.…”

Section: Introductionmentioning

confidence: 99%

Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data

Klein,

Phung,

Hajnal

et al. 2023

PLoS ONE

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Stochastic population models are widely used to model phenomena in different areas such as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond. Quantitative analysis of stochastic population models easily becomes challenging due to the combinatorial number of possible states of the population. Moreover, while the modeller easily hypothesises the mechanistic aspects of the model, the quantitative parameters associated to these mechanistic transitions are difficult or impossible to measure directly. In this paper, we investigate how formal verification methods can aid parameter inference for population discrete-time Markov chains in a scenario where only a limited sample of population-level data measurements—sample distributions among terminal states—are available. We first discuss the parameter identifiability and uncertainty quantification in this setup, as well as how the existing techniques of formal parameter synthesis and Bayesian inference apply. Then, we propose and implement four different methods, three of which incorporate formal parameter synthesis as a pre-computation step. We empirically evaluate the performance of the proposed methods over four representative case studies. We find that our proposed methods incorporating formal parameter synthesis as a pre-computation step allow us to significantly enhance the accuracy, precision, and scalability of inference. Specifically, in the case of unidentifiable parameters, we accurately capture the subspace of parameters which is data-compliant at a desired confidence level.

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“…However, due to the intrinsic spatial-heterogeneity in collective systems (the same agent can exhibit different behaviour in different positions), the analytical model can be unresolvable due to the number of coupled ODEs in the model. Moreover, the question also arises of whether the fractured population is large enough to justify fluid/moment closure techniques [6]. As a result, in many circumstances, stochastic simulation is the only option to analyse such models.…”

Section: Introductionmentioning

confidence: 99%

Speed-Up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction

Feng

Hillston

2015

Computer Performance Engineering

Self Cite

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Abstract. We present a novel statistical model reduction method which can significantly boost the speed of stochastic simulation of a population continuous-time Markov chain (PCTMC) model. This is achieved by identifying and removing agent types and transitions from the simulation which have only minor impact on the evolution of population dynamics of target agent types specified by the modeller. The error induced on the target agent types can be measured by a normalized coupling coefficient, which is calculated by an error propagation method over a directed relation graph for the PCTMC, using a limited number of simulation runs of the full model. Those agent types and transitions with minor impact are safely removed without incurring a significant error on the simulation result. To demonstrate the approach, we show the usefulness of our statistical reduction method by applying it to 50 randomly generated PCTMC models corresponding to different city bike-sharing scenarios.

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Challenges for Quantitative Analysis of Collective Adaptive Systems

Cited by 9 publications

References 19 publications

Toward Formal Models and Languages for Verifiable Multi-Robot Systems

Toward Formal Models and Languages for Verifiable Multi-Robot Systems

Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data

Speed-Up of Stochastic Simulation of PCTMC Models by Statistical Model Reduction

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