Consensus reaching in swarms ruled by a hybrid metric-topological distance

Recent empirical observations of three-dimensional bird flocks and human crowds have challenged the long-prevailing assumption that a metric interaction distance rules swarming behaviors. In some cases, individual agents are found to be engaged in local information exchanges with a fixed number of neighbors, i.e. a topological interaction. However, complex system dynamics based on pure metric or pure topological distances both face physical inconsistencies in low and high density situations. Here, we propose a hybrid metric-topological interaction distance overcoming these issues and enabling a real-life implementation in artificial robotic swarms. We use network- and graph-theoretic approaches combined with a dynamical model of locally interacting self-propelled particles to study the consensus reaching pro- cess for a swarm ruled by this hybrid interaction distance. Specifically, we establish exactly the probability of reaching consensus in the absence of noise. In addition, simulations of swarms of self-propelled particles are carried out to assess the influence of the hybrid distance and noise

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

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

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