1,720,988 research outputs found
Engineering Distributed Collective Intelligence in Cyber-Physical Swarms
No full textCyber-physical swarms represent a paradigm shift in distributed systems, mirroring characteristics akin to natural swarms, such as self-organization, scalability, and fault tolerance. This paper delves into these complex systems, characterized by vast networks of cyber-physical entities with limited environmental awareness, yet capable of exhibiting emergent collective behaviors. These systems encompass a diverse array of scenarios, ranging from swarm robotics to the interconnectivity in smart cities, as well as the collaboration among augmented humans. The engineering of such systems presents unique challenges, primarily due to their intricate complexity and the spontaneous nature of their collective behaviors.This paper aims to dissect these challenges, offering a clear delineation of potential approaches. We present a comprehensive analysis, shedding light on the intricacies of engineering cyber-physical swarms and discussing modern solutions in engineering collective applications for such systems
MacroSwarm: A scala framework for swarm programming
Full text linkProgramming swarm behaviors is a challenging task, due to the need to express collective behaviors in terms of local interactions among simple agents. Even if several programming frameworks have been proposed, they are often based on low-level abstractions, which makes the development of swarm applications complex and error-prone. Thus, we present MacroSwarm, an aggregate programming framework for the development of swarm behaviors. With this framework, it is possible to define a large variety of swarm behaviors, starting from simple movements to more complex ones, such as aggregation, flocking, and collective decision-making. In this paper, we present the main features of the framework and some simple examples of its API usage
MacroSwarm: A Field-Based Compositional Framework for Swarm Programming
Full text linkSwarm behaviour engineering is an area of research that seeks to investigate methods for coordinating computation and action within groups of simple agents to achieve complex global goals like collective movement, clustering, and distributed sensing. Despite recent progress in the study and engineering of swarms (of drones, robots, vehicles), there is still need for general design and implementation methods that can be used to define complex swarm coordination in a principled way. To face this need, this paper proposes a new field-based coordination approach, called MacroSwarm, to design fully composable and reusable blocks of swarm behaviour. Based on the macroprogramming approach of aggregate computing, it roots on the idea of modelling each block of swarm behaviour by a purely functional transformation of sensing fields into actuation description fields, typically including movement vectors. We showcase the potential of MacroSwarm as a framework for collective intelligence by simulation, in a variety of scenarios including flocking, morphogenesis, and collective decision-making
MacroSwarm: A Field-based Compositional Framework for Swarm Programming
Full text linkSwarm behaviour engineering is an area of research that seeks to investigate methods and techniques for coordinating computation and action within groups of simple agents to achieve complex global goals like pattern formation, collective movement, clustering, and distributed sensing. Despite recent progress in the analysis and engineering of swarms (of drones, robots, vehicles), there is still a need for general design and implementation methods and tools that can be used to define complex swarm behaviour in a principled way. To contribute to this quest, this article proposes a new field-based coordination approach, called MacroSwarm, to design and program swarm behaviour in terms of reusable and fully composable functional blocks embedding collective computation and coordination. Based on the macroprogramming paradigm of aggregate computing, MacroSwarm builds on the idea of expressing each swarm behaviour block as a pure function, mapping sensing fields into actuation goal fields, e.g., including movement vectors. In order to demonstrate the expressiveness, compositionality, and practicality of MacroSwarm as a framework for swarm programming, we perform a variety of simulations covering common patterns of flocking, pattern formation, and collective decision-making. The implications of the inherent self-stabilisation properties of field-based computations in MacroSwarm are discussed, which formally guarantee some resilience properties and guided the design of the library
Systematic review of urinary incontinence and overactive bladder cost-of-illness studies
No full textObjective: Systematise all the international available evidence on the burden of disease because of UI and OAB on society.
Materials and Methods: A systematic search of Medline and Embase databases was conducted on June 30th, 2008, aimed at retrieving studies concerning the cost of UI and OAB, any time.
Results: Out of 161 abstracts retrieved, 25 studies were included in the review. Key findings emerged from the review process: (i) prevalence rates vary depending upon definitions used, populations studied, and methods employed, (ii) estimates of direct healthcare costs should take into account the hidden nature of incontinence since the most affected individuals do not seek treatment, (iii) biases may occur when estimating the burden of disease using claims data as these concern only people seeking care and treated for their symptoms, and (iv) direct costs of incontinence would likely be higher, if a greater proportion of patients with UI and/or OAB sought care. From an economic perspective, investing more resources in early diagnosis and initial treatment could potentially reduce the costs of treating late-stage disease and its consequences. This study illustrates also that healthcare systems never pursued clearly this direction: in OAB communitydwellers the cost of diagnosing and treating is less than the cost of treating its related consequences (e.g. skin irritations, urinary tract infections, falls), 29% and 48.4% of direct costs respectively. Whilst in UI community-dwellers, the cost of treating consequences is still high, being 18.2% of direct costs.
Conclusions: UI and OAB are associated with significant cost to the individual, institution and society. Understanding the magnitude of the impact of these pelvic floor disorders is important to health care providers, payers, and public policymakers in establishing health care priorities, taking advantage of potential savings, and allocating scarce resources for its appropriate management
Impact on Quality of Life of Urinary Incontinence and Overactive Bladder: A Systematic Literature Review
No full textThe paper provides a systematization of the scientific evidence on quality of life of patients affected by urinary incontinence (UI) and overactive bladder (OAB) through a systematic literature review. A single search strategy was performed through the databases and papers collected are reviewed by independent researchers finally, including 39 papers. A strong heterogeneity of studies emerged from the evidence. The multidimensionality of the consequences produced by UI and OAB increased the attention on the identification of the most affected dimension of life quality (i.e. physical, emotional) and on the attempt of predicting life quality impairment through specific variables. © 2010 Elsevier Inc. All rights reserved
A programming approach to collective autonomy
Full text linkResearch and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy
Machine Learning for Aggregate Computing: a Research Roadmap
Full text linkAggregate computing is a macro-approach for programming collective intelligence and self-organisation in distributed systems. In this paradigm, a single 'aggregate program' drives the collective behaviour of the system, provided that the agents follow an execution protocol consisting of asynchronous sense-compute-act rounds. For actual execution, a proper aggregate computing middleware or platform has to be deployed across the nodes of the target distributed system, to support the services needed for the execution of applications. Overall, the engineering of aggregate computing applications is a rich activity that spans multiple concerns including designing the aggregate program, developing reusable algorithms, detailing the execution model, and choosing a deployment based on available infrastructure. Traditionally, these activities have been carried out through ad-hoc designs and implementations tailored to specific contexts and goals. To overcome the complexity and cost of manually tailoring or fixing algorithms, execution details, and deployments, we propose to use machine learning techniques, to automatically create policies for applications and their management. To support such a goal, we detail a rich research roadmap, showing opportunities and challenges of integrating aggregate computing and learning
Low-code design of collective systems with ScaFi-Blocks
No full textScaFi-Blocks is a visual, low-code programming environment for designing and implementing swarm algorithms. Built on the ScaFi aggregate computing framework and the Blockly visual programming library, ScaFi-Blocks enables users to visually compose algorithms using intuitive building blocks, abstracting away the complexities of traditional swarm programming frameworks. This approach simplifies the development of collective behaviours for a wide range of swarm systems, including robot swarms, IoT device ensembles, and sensor networks, fostering broader accessibility and innovation within the field. This contribution bridges the gap between visual programming and textual code, lowering the barrier to entry for non-experts while promoting a deeper understanding of aggregate computing principles
Learning Opportunities in Collective Adaptive Systems
Full text linkIn collective systems, a multitude of computational agents coordinate to achieve a system goal beyond their individual capabilities. These systems are typically deployed in dynamic and partially unknown environments, where system designers cannot anticipate all potential situations, events, and faults that agents may experience. For this reason, such systems are often adaptive, that is, able to change their behavior to tolerate contingencies or embrace novel opportunities—becoming Collective Adaptive Systems (CAS). When engineering CAS, it is crucial for the designer to take into account various essential aspects, such as deployment strategies, coordination policies for distributed execution, and the application logic itself. For each of these, learning could be a precious tool at designers’ disposal, as it enables both design-time support and run-time adaptation with minimal a priori knowledge. Therefore, in this chapter, we first provide a brief overview of how learning has been applied in CAS so far. Then, we describe a few novel opportunities. Finally, we discuss potential future applications of learning, particularly within the context of the Fluidware vision for pervasive systems programming
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