Developing a Methodology to Characterize the Use of Emerging and Converging Technologies in Federal Agencies

Although some methodologies exist for the systematic and strategic consideration of emerging and converging technologies, they typically do not incorporate agency current use, strategies, or foresight. This research develops a methodology to characterize current and potential United States federal agency use of emerging and converging technologies to fulfill agency strategic plans and serve society. Phase 1 of this research develops a methodology to fulfill criteria derived from a literature review and an assessment of best practices. Designed to be implemented in four phases—develop, apply, evaluate, disseminate—the steps of this methodology include definition, collection, organization, analysis, synthesis, evaluation, and dissemination. Within the analyze step, a mix of qualitative and quantitative analysis approaches are applied to answer the defined questions. Current agency use of emerging and converging technologies is characterized with content analysis of strategic documents; technology assessment analysis by experts; and individual interviews with government employees. Potential agency use of emerging and converging technologies is characterized with individual interviews with government employees; plausibility matrix analysis by experts; and crowd-sourced intelligence. The methodology is applied in Phase 2 to two cases, the Department of Commerce and the Department of Energy, then evaluated in Phase 3 versus the design criteria and visual analytics, and disseminated in Phase 4 to researchers, policymakers, and the general public. Key findings, results, and meta-inferences of this research are that many more potential uses exist for using emerging and converging technologies to fulfill agency strategies and the research identifies some of the potential uses by technology and strategy. These potential uses also are presented in terms of comparable technical feasibility and societal benefit. Implications for policymakers are that governing with foresight is critical; encouraging systematic agency consideration of emerging and converging technologies is necessary; and it is important to implement a government-wide methodology that will characterize current and potential use of emerging and converging technologies for fulfilling agency strategies. This research contributes the criterion for such a methodology as well as the methodology and the results of its application to two agency cases

Artificial Intelligence Aggregating Opinions of a Group of People

This study deals with the problems of aggregating the opinions of a group of people in such a way that the quality of the group decision surpasses the quality of the decision of the most experienced individual within the group. The methods we have studied fall in the research domain of the so called collective intelligence. We provide an overview of the state-of-the-art in the collective intelligence. We describe the method based on adaptive boosting we have proposed aggregatig the opinions of a group of people. We have implemented a web application to gather opinions of people and used the application to collect data for the experimental analysis. The model problem was to identify whether there is or there is not a tumor present in the series of X-ray images of human lungs. We have compared our proposed method to conventional methods such as majority voting. We have concluded that our proposed method can be successfully used to aggregate opinions of a group of people to increase their collective intelligence above the level of the most successful individual within the group in many cases. We have observed that the highest increase in the collective intelligence may be achieved for intelligence wise homogeneous groups what confirms the results of previous studies

Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

A survey of modern exogenous fault detection and diagnosis methods for swarm robotics

Swarm robotic systems are heavily inspired by observations of social insects. This often leads to robust-ness being viewed as an inherent property of them. However, this has been shown to not always be thecase. Because of this, fault detection and diagnosis in swarm robotic systems is of the utmost importancefor ensuring the continued operation and success of the swarm. This paper provides an overview of recentwork in the field of exogenous fault detection and diagnosis in swarm robotics, focusing on the four areaswhere research is concentrated: immune system, data modelling, and blockchain-based fault detectionmethods and local-sensing based fault diagnosis methods. Each of these areas have significant advan-tages and disadvantages which are explored in detail. Though the work presented here represents a sig-nificant advancement in the field, there are still large areas that require further research. Specifically,further research is required in testing these methods on real robotic swarms, fault diagnosis methods,and integrating fault detection, diagnosis and recovery methods in order to create robust swarms thatcan be used for non-trivial tasks

Control to flocking of the kinetic Cucker-Smale model

The well-known Cucker-Smale model is a macroscopic system reflecting flocking, i.e. the alignment of velocities in a group of autonomous agents having mutual interactions. In the present paper, we consider the mean-field limit of that model, called the kinetic Cucker-Smale model, which is a transport partial differential equation involving nonlocal terms. It is known that flocking is reached asymptotically whenever the initial conditions of the group of agents are in a favorable configuration. For other initial configurations, it is natural to investigate whether flocking can be enforced by means of an appropriate external force, applied to an adequate time-varying subdomain. In this paper we prove that we can drive to flocking any group of agents governed by the kinetic Cucker-Smale model, by means of a sparse centralized control strategy, and this, for any initial configuration of the crowd. Here, "sparse control" means that the action at each time is limited over an arbitrary proportion of the crowd, or, as a variant, of the space of configurations; "centralized" means that the strategy is computed by an external agent knowing the configuration of all agents. We stress that we do not only design a control function (in a sampled feedback form), but also a time-varying control domain on which the action is applied. The sparsity constraint reflects the fact that one cannot act on the whole crowd at every instant of time. Our approach is based on geometric considerations on the velocity field of the kinetic Cucker-Smale PDE, and in particular on the analysis of the particle flow generated by this vector field. The control domain and the control functions are designed to satisfy appropriate constraints, and such that, for any initial configuration, the velocity part of the support of the measure solution asymptotically shrinks to a singleton, which means flocking