Organisational Abstractions for the Analysis and Design of Multi-Agent Systems

The architecture of a multi-agent system can naturally be viewed as a computational organisation. For this reason, we believe organisational abstractions should play a central role in the analysis and design of such systems. To this end, the concepts of agent roles and role models are increasingly being used to specify and design multi-agent systems. However, this is not the full picture. In this paper we introduce three additional organisational concepts - organisational rules, organisational structures, and organisational patterns - that we believe are necessary for the complete specification of computational organisations. We view the introduction of these concepts as a step towards a comprehensive methodology for agent-oriented systems

Organisational Rules as an Abstraction for the Analysis and Design of Multi-Agent Systems

Multi-agent systems can very naturally be viewed as computational organisations. For this reason, we believe organisational abstractions offer a promising set of metaphors and models that can be exploited in the analysis and design of such systems. To this end, the concept of role models is increasingly being used to specify and design multi-agent systems. However, this is not the full picture. In this paper we introduce three additional organisational concepts - organisational rules , organisational structures, and organisational patterns - and discuss why we believe they are necessary for the complete specification of computational organisations. In particular, we focus on the concept of organisational rules and introduce a formalism, based on temporal logic, to specify them. This formalism is then used to drive the definition of the organisational structure and the identification of the organisational patterns. Finally, the paper sketches some guidelines for a methodology for agent-oriented systems based on our expanded set of organisational abstractions

Towards Adaptive Flow Programming for the IoT: The Fluidware Approach

The objective of this position paper is to present Fluidware, a proposal towards an innovative programming model for the IoT, conceived to ease the development of flexible and robust large-scale IoT services and applications. The key innovative idea of Fluidware is to abstract collectives of devices of the IoT fabric as sources, digesters, and targets of distributed 'flows' of contextualized events, carrying information about data produced and actuating commands. Accordingly, programming services and applications implies declaratively specifying 'funnel processes' to channel, elaborate, and re-direct such flows in a fully-distributed way, as a means to coordinate the activities of devices and realize services and applications. The potential applicability of Fluidware and its expected advantages are exemplified via example in the area of ambient assisted living

Time-fluid field-based coordination

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively from a global perspective, in terms of functional manipulation and evolution in “space and time” of distributed data structures, called fields. More specifically, regarding time, in field-based coordination it is assumed that local activities in each device, called computational rounds, are regulated by a fixed clock, typically, a fair and unsynchronized distributed scheduler. In this work, we challenge this assumption, and propose an alternative approach where the round execution scheduling is naturally programmed along with the usual coordination specification, namely, in terms of a field of causal relations dictating what is the notion of causality (why and when a round has to be locally scheduled) and how it should change across time and space. This abstraction over the traditional view on global time allows us to express what we call “time-fluid” coordination, where causality can be finely tuned to select the event triggers to react to, up to to achieve improved balance between performance (system reactivity) and cost (usage of computational resources). We propose an implementation in the aggregate computing framework, and evaluate via simulation on a case study

Time-fluid field-based coordination

Emerging application scenarios, such as cyber-physical systems (CPSs), the Internet of Things (IoT), and edge computing, call for coordination approaches addressing openness, self-adaptation, heterogeneity, and deployment agnosticism. Field-based coordination is one such approach, promoting the idea of programming system coordination declaratively from a global perspective, in terms of functional manipulation and evolution in \u201cspace and time\u201d of distributed data structures, called fields. More specifically, regarding time, in field-based coordination it is assumed that local activities in each device, called computational rounds, are regulated by a fixed clock, typically, a fair and unsynchronized distributed scheduler. In this work, we challenge this assumption, and propose an alternative approach where the round execution scheduling is naturally programmed along with the usual coordination specification, namely, in terms of a field of causal relations dictating what is the notion of causality (why and when a round has to be locally scheduled) and how it should change across time and space. This abstraction over the traditional view on global time allows us to express what we call \u201ctime-fluid\u201d coordination, where causality can be finely tuned to select the event triggers to react to, up to to achieve improved balance between performance (system reactivity) and cost (usage of computational resources). We propose an implementation in the aggregate computing framework, and evaluate via simulation on a case study

Space-Fluid Adaptive Sampling: A Field-Based, Self-organising Approach

A recurrent task in coordinated systems is managing (estimating, predicting, or controlling) signals that vary in space, such as distributed sensed data or computation outcomes. Especially in large-scale settings, the problem can be addressed through decentralised and situated computing systems: nodes can locally sense, process, and act upon signals, and coordinate with neighbours to implement collective strategies. Accordingly, in this work we devise distributed coordination strategies for the estimation of a spatial phenomenon through collaborative adaptive sampling. Our design is based on the idea of dynamically partitioning space into regions that compete and grow/shrink to provide accurate aggregate sampling. Such regions hence define a sort of virtualised space that is “fluid”, since its structure adapts in response to pressure forces exerted by the underlying phenomenon. We provide an adaptive sampling algorithm in the field-based coordination framework. Finally, we verify by simulation that the proposed algorithm effectively carries out a spatially adaptive sampling

Is Tuber brumale a threat to T. melanosporum and T. aestivum plantations?

True truffles in the genus Tuber are the most valuable ectomycorrhizal fungiand their cultivation has become widespread around the world. Competition with other ectomycorrhizal fungi and especially with undesired Tuber species, like T. brumale, can threaten the success of a truffle plantation. In this work, the competitiveness of T. brumale towards T. melanosporum and T. aestivum was assessed in a 14 year-old plantation carried out planting seedlings inoculated with these three truffle species in adjacent plots. Analyses of both truffle ectomycorrhizas and extra-radical mycelium were carried out in the transects separating the T. brumale plot from T. melanosporum and T. aestivum plots. The results confirm the competitiveness of T. brumale against T. aestivum and T. melanosporum due to its major ability to colonize the soil around its ectomycorrhizas. However, its competitiveness is limited to the transect areas and it was never found inside T. melanosporum plot. These results remark that, in presence of optimal conditions for T. melanosporum and T. aestivum, the greatest risk of contamination with T. brumale is due to wrong greenhouse activity

Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service

A complex combination of clinical, demographic and lifestyle parameters determines the correct diagnosis and the most effective treatment for asthma and Chronic Obstructive Pulmonary Disease patients. Artificial Intelligence techniques help clinicians in devising the correct diagnosis and designing the most suitable clinical pathway accordingly, tailored to the specific patient conditions. In the case of machine learning (ML) approaches, availability of real-world patient clinical data to train and evaluate the ML pipeline deputed to assist clinicians in their daily practice is crucial. However, it is common practice to exploit either synthetic data sets or heavily preprocessed collections cleaning and merging different data sources. In this paper, we describe an automated ML pipeline designed for a real-world data set including patients from a Dutch primary care service, and provide a performance comparison of different prediction models for (i) assessing various clinical parameters, (ii) designing interventions, and (iii) defining the diagnosis