165 research outputs found
Modelling movement for collective adaptive systems with CARMA
Space and movement through space play an important role in many collective
adaptive systems (CAS). CAS consist of multiple components interacting to
achieve some goal in a system or environment that can change over time. When
these components operate in space, then their behaviour can be affected by
where they are located in that space. Examples include the possibility of
communication between two components located at different points, and rates of
movement of a component that may be affected by location. The CARMA language
and its associated software tools can be used to model such systems. In
particular, a graphical editor for CARMA allows for the specification of
spatial structure and generation of templates that can be used in a CARMA model
with space. We demonstrate the use of this tool to experiment with a model of
pedestrian movement over a network of paths.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200
Statistical analysis of CARMA models: an advanced tutorial
CARMA (Collective Adaptive Resource-sharing Markovian Agents) is a process-algebra-based quantitative language developed for the modeling of collective adaptive systems. A CARMA model consists of an environment in which a collective of components with attribute stores interact via unicast and broadcast communication, providing a rich modeling formalism. The semantics of a CARMA model are given by a continuous-time Markov chain which can be simulated using the CARMA Eclipse Plug-in. Furthermore, statistical model checking can be applied to the trajectories generated through simulation using the MultiVeStA tool. This advanced tutorial will introduce some of the theory behind CARMA and MultiVeStA as well as demonstrate its application to collective adaptive system modeling
On Formal Methods for Collective Adaptive System Engineering. {Scalable Approximated, Spatial} Analysis Techniques. Extended Abstract
In this extended abstract a view on the role of Formal Methods in System
Engineering is briefly presented. Then two examples of useful analysis
techniques based on solid mathematical theories are discussed as well as the
software tools which have been built for supporting such techniques. The first
technique is Scalable Approximated Population DTMC Model-checking. The second
one is Spatial Model-checking for Closure Spaces. Both techniques have been
developed in the context of the EU funded project QUANTICOL.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200
Stochastic modelling of spatial collective adaptive systems
Collective Adaptive Systems (CAS) are composed of individual agents with internal
knowledge and rules which organize themselves into ensembles. These ensembles can
often be observed to exhibit behaviour resembling that of a single entity with a clear
goal and a consistent internal knowledge, even when the individual agents within the
ensemble are not managed by any outside, globally-accessible entity.
Because of their lack of a need for centralized control which results in high robustness,
CAS are commonly observed in nature – and for similar reasons are often reflected
in human engineered systems. Researching the patterns of operation observed in such
systems provides meaningful insight into how to design and optimise stable multiagent
systems capable of withstanding adverse conditions. Formal modelling provides
valuable intellectual tools which can be applied to the problem of analysis of systems
by means of modelling and simulation.
In this thesis we explore the modelling of CAS in which space (topology and distances)
plays a significant role. Working with CARMA (Collective Adaptive Resource-sharing
Markovian Agents) a formal feature-rich language for modelling stochastic CAS, we
investigate a number of spatial CAS scenarios from the realm of urban planning. When
components operate in a spatial context, their behaviour can be affected by where they
are located in that space. For example, their location can influence the speed at which
they move, and their ability to communicate with other components.
Components in CARMA have internal store, and behaviour expressed by Markov processes.
They can communicate with each other through sending messages on state
transitions in a unicast or broadcast fashion. Simulation with pseudo-random events
can be used to obtain values of measures applied to CARMA models, providing a basis
for analysis and optimisation.
The CARMA models developed in the case studies are data-driven and the results of
simulating these models are compared with real-world data. In particular, we explore
two scenarios: crowd-routing and city transportation systems.
Building on top of CARMA, we also introduce CGP (CARMA Graphical Plugin), a
novel graphical software tool for graphically specifying spatial CAS systems with the
feature of automatic translation into CARMA models. We also supply CARMA with
additional syntax structures for expressing spatial constructs
Specification and Analysis of Open-Ended Systems with CARMA
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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