20 research outputs found
Stochastic and Spatial Equivalences for PALOMA
We concentrate our study on a recent process algebra - PALOMA - intended to
capture interactions between spatially distributed agents, for example in
collective adaptive systems. New agent-based semantic rules for deriving the
underlying continuous time Markov chain are given in terms of State to Function
Labelled Transition Systems. Furthermore we define a bisimulation with respect
to an isometric transformation of space allowing us to compare PALOMA models
with respect to their relative rather than absolute locations.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200
CARMA Eclipse plug-in: A tool supporting design and analysis of Collective Adaptive Systems
Collective Adaptive Systems (CAS) are heterogeneous populations of autonomous task-oriented agents that cooperate on common goals forming a collective system. 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. Existing tools and languages are typically not able to describe the complex interactions that underpin such systems, which operate in a highly dynamic environment. For this reason, recently, new formalisms have been proposed to model CAS. One such is Carma, a process specification language that is equipped with linguistic constructs specifically developed for modelling and programming systems that can operate in open-ended and unpredictable environments. In this paper we present the Carma Eclipse plug-in, a toolset integrated in Eclipse, developed to support the design and analysis of CAS
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 âcollectiveâ, consists of a set of components, each of which exhibits a set of attributes. To model dynamic aggregations, which are sometimes referred to as âensemblesâ, 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
Collective Adaptive Systems: Qualitative and Quantitative Modelling and Analysis (Dagstuhl Seminar 14512)
This report documents the program and the outcomes of Dagstuhl Seminar 14512 "Collective Adaptive Systems: Qualitative and Quantitative Modelling and Analysis". Besides presentations on current work in the area, the seminar focused on the following topics:
(i) Modelling techniques and languages for collective adaptive systems based on the above formalisms. (ii) Verification of collective adaptive systems. (iii) Humans-in-the-loop in collective adaptive systems
CARMA: Collective Adaptive Resource-sharing Markovian Agents
In this paper we present CARMA, a language recently defined to support specification and analysis of collective adaptive systems. CARMA 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 that are 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. In this paper, we first introduce the basic principles of CARMA and then we show how our language can be used to support specification with a simple but illustrative example of a socio-technical collective adaptive system
Modelling and Analysis of Collective Adaptive Systems with CARMA and its Tools
Collective Adaptive Systems (CAS) are heterogeneous collections of autonomous task-oriented systems that cooperate on common goals forming a collective system. 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.
This chapter presents Carma, a language recently defined to support specification and analysis of collective adaptive systems, and its tools developed for supporting system design and analysis. Carma is equipped with linguistic constructs specifically developed for modelling and programming systems that can operate in open-ended and unpredictable environments. The chapter also presents the Carma Eclipse plug-in that allows Carma models to be specified by means of an appropriate high-level language. Finally, we show how Carma and its tools can be used to support specification with a simple but illustrative example of a socio-technical collective adaptive system
Continuous-time temporal logic specification and verification for nonlinear biological systems in uncertain contexts
In this thesis we introduce a complete framework for modelling and verification of biological systems in uncertain contexts based on the bond-calculus process algebra and
the LBUC spatio-temporal logic. The bond-calculus is a biological process algebra which
captures complex patterns of interaction based on affinity patterns, a novel communication
mechanism using pattern matching to express multiway interaction affinities and general
kinetic laws, whilst retaining an agent-centric modelling style for biomolecular species.
The bond-calculus is equipped with a novel continuous semantics which maps models to
systems of Ordinary Differential Equations (ODEs) in a compositional way.
We then extend the bond-calculus to handle uncertain models, featuring interval uncertainties in their species concentrations and reaction rate parameters. Our semantics is also
extended to handle uncertainty in every aspect of a model, producing non-deterministic
continuous systems whose behaviour depends either on time-independent uncertain parameters and initial conditions, corresponding to our partial knowledge of the system at
hand, or time-varying uncertain inputs, corresponding to genuine variability in a systemâs
behaviour based on environmental factors.
This language is then coupled with the LBUC spatio-temporal logic which combines
Signal Temporal Logic (STL) temporal operators with an uncertain context operator
which quantifies over an uncertain context model describing the range of environments
over which a property must hold. We develop model-checking procedures for STL and
LBUC properties based on verified signal monitoring over flowpipes produced by the
Flow* verified integrator, including the technique of masking which directs monitoring for
atomic propositions to time regions relevant to the overall verification problem at hand.
This allows us to monitor many interesting nested contextual properties and frequently
reduces monitoring costs by an order of magnitude. Finally, we explore the technique
of contextual signal monitoring which can use a single Flow* flowpipe representing a
functional dependency to complete a whole tree of signals corresponding to different
uncertain contexts. This allows us to produce refined monitoring results over the whole
space and to explore the variation in system behaviour in different contexts
Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning
In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations