PALOMA: A Process Algebra for Located Markovian Agents

Feng, Cheng; Hillston, Jane

doi:10.1007/978-3-319-10696-0_22

Cited by 31 publications

(36 citation statements)

References 17 publications

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“…Whilst some offer support for large populations of agents, they have not been designed with CAS in mind, and typically only support synchronous unicast communication. In recent years there have been languages targeted towards systems consisting of populations, collectives or ensembles of agents [12][13][14], which feature attribute-based communication and explicit representation of locations and the development of CARMA has been informed by these.…”

Section: Related Workmentioning

confidence: 99%

“…PALOMA [13] is a process algebra that takes as starting point a model based on located Markovian agents each of which is parameterised by a location, which can be regarded as an attribute of the agent. The ability of agents to communicate depends on their location, through a perception function, which can be used to define the range of a communication.…”

Section: Related Workmentioning

confidence: 99%

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Specification and Analysis of Open-Ended Systems with CARMA

Hillston

Loreti

2015

Lecture Notes in Computer Science

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Abstract. CARMA is a new language recently defined to support quantified specification and analysis of collective adaptive systems. It is a stochastic process algebra equipped with linguistic constructs specifically developed for modelling and programming systems that can operate in open-ended and unpredictable environments. This class of systems is typically composed of a huge number of interacting agents that dynamically adjust and combine their behaviour to achieve specific goals. A CARMA model, termed a "collective", consists of a set of components, each of which exhibits a set of attributes. To model dynamic aggregations, which are sometimes referred to as "ensembles", CARMA provides communication primitives based on predicates over the exhibited attributes. These predicates are used to select the participants in a communication. Two communication mechanisms are provided in the CARMA language: multicast-based and unicast-based. A key feature of CARMA is the explicit representation of the environment in which processes interact, allowing rapid testing of a system under different open world scenarios. The environment in CARMA models can evolve at runtime, due to the feedback from the system, and it further modulates the interaction between components, by shaping rates and interaction probabilities.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Specification and Analysis of Open-Ended Systems with CARMA

Hillston

Loreti

2015

Lecture Notes in Computer Science

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“…Here we present the PALOMA model for the template city bike-sharing scenario which can be automatically parsed to a PCTMC model via the population semantics introduced in [10]. Suppose that there are n bike stations in the city, and each one has a number of available bikes and slots.…”

Section: Model Definitionmentioning

confidence: 99%

“…The model is specified in PALOMA [10], a process algebra recently designed for the modelling of collective adaptive systems, which makes it easy to generate many variations of the same model. The underlying PCTMC model can be automatically generated according to the population semantics of this modelling language.…”

Section: Introductionmentioning

confidence: 99%

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

Feng

Hillston

2015

Computer Performance Engineering

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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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“…Furthermore, CARMA allows the environment within which the model components interact to be defined separately from the components. The development of CARMA has been influenced by a number of previous process algebra including the Markovian process algebra PEPA [12], the location-focussed PALOMA [9] and SCEL [8] which uses attribute-based communication. Attribute-based communcation is explored further in the process calculus AbC [2].…”

Section: Introductionmentioning

confidence: 99%

Modelling Ambulance Deployment with CarmaCARMA

Galpin

2016

Lecture Notes in Computer Science

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Abstract. CARMA is process-algebra influenced language for the quantitative modelling of collective adaptive systems which involve collaboration and coordination. These systems consist of multiple components that interact to achieve certain goals and that adapt to changes in the environment. As a case study for the application of CARMA, this paper presents an ambulance deployment system where ambulances go to medical incidents and either treat patients at the scene or transfer them to hospital. The Eclipse CARMA Plug-in is used to simulate the system, and demonstrate its behaviour in different circumstances.

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PALOMA: A Process Algebra for Located Markovian Agents

Cited by 31 publications

References 17 publications

Specification and Analysis of Open-Ended Systems with CARMA

Specification and Analysis of Open-Ended Systems with CARMA

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

Modelling Ambulance Deployment with CarmaCARMA

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