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

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

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

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

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

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

Collaborate for what: a structural topic model analysis on CDP data

[EN] The aim of this paper is to understand why firms engage with their suppliers to collaborate for sustainability. To this purpose, we use the Carbon Disclosure Project (CDP) Supply Chain dataset and apply the Structural Topic Model to 1) identify the topics discussed in an open-ended question related to climate-related supplier engagement and 2) estimate the differences in the discussion of such topics between CDP members and non-members, respectively focal firms and first-tier suppliers. The analysis highlights that the two most prevalent reasons firms engage with their suppliers relate to several aspects of the management of the supply chain, and the services and goods mobility efficiency. It is further noted how first-tier suppliers do not dispose of established capabilities and, therefore, are still in the course of improving their processes. On the contrary, focal firms have more structured capabilities so to manage supplier engagement for information collection. This study demonstrates how big data and machine learning methods can be applied to analyse unstructured textual data from traditional surveys.Salvatore, C.; Madonna, A.; Bianchi, A.; Boffelli, A.; Kalchschmidt, M. (2022). Collaborate for what: a structural topic model analysis on CDP data. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 139-146. https://doi.org/10.4995/CARMA2022.2022.1507413914