143 research outputs found

    Models of Consensus for Multiple Agent Systems

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    Models of consensus are used to manage multiple agent systems in order to choose between different recommendations provided by the system. It is assumed that there is a central agent that solicits recommendations or plans from other agents. That agent the n determines the consensus of the other agents, and chooses the resultant consensus recommendation or plan. Voting schemes such as this have been used in a variety of domains, including air traffic control. This paper uses an analytic model to study the use of consensus in multiple agent systems. The binomial model is used to study the probability that the consensus judgment is correct or incorrect. That basic model is extended to account for both different levels of agent competence and unequal prior odds. The analysis of that model is critical in the investigation of multiple agent systems, since the model leads us to conclude that in some cases consensus judgment is not appropriate. In addition, the results allow us to determine how many agents should be used to develop consensus decisions, which agents should be used to develop consensus decisions and under which conditions the consensus model should be used.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994

    Do macro-forecasters herd?

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    We show that typical tests of whether forecasters herd will falsely indicate herding behaviour for a variety of types of behaviour and forecasting environments that give rise to disagreement amongst forecasters. We establish that forecasters will appear to herd if di¤erences between them reject noise as opposed to private information, or if they arise from informational rigidities. Noise can have a behavioural interpretation, and if so will depend on the behavioural model under consideration. An application of the herding tests to US quarterly survey forecasts of inflation and output growth data 1981-2013 does not support herding behaviour

    The Wooster Voice (Wooster, OH), 1978-01-20

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    This edition of the Voice includes articles about the lack of tenure opportunities for female faculty at Wooster, Women\u27s Career Day, noise pollution and stereo volume in dorms on campus, the Soup and Bread program, human rights in the U.S.S.R., a symposium series on Europe between the World Wars opened with lecture by Henry Copeland, a review of a record by Genesis, help for American medical students studying overseas, and medical school programs.https://openworks.wooster.edu/voice1971-1980/1184/thumbnail.jp

    The Crescent Student Newspaper, March 9, 2010

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    Student newspaper of George Fox University.https://digitalcommons.georgefox.edu/the_crescent/2342/thumbnail.jp

    The Wooster Voice (Wooster, OH), 1978-01-20

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    This edition of the Voice includes articles about the lack of tenure opportunities for female faculty at Wooster, Women\u27s Career Day, noise pollution and stereo volume in dorms on campus, the Soup and Bread program, human rights in the U.S.S.R., a symposium series on Europe between the World Wars opened with lecture by Henry Copeland, a review of a record by Genesis, help for American medical students studying overseas, and medical school programs.https://openworks.wooster.edu/voice1971-1980/1184/thumbnail.jp

    The Wooster Voice (Wooster, OH), 1978-01-20

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    This edition of the Voice includes articles about the lack of tenure opportunities for female faculty at Wooster, Women\u27s Career Day, noise pollution and stereo volume in dorms on campus, the Soup and Bread program, human rights in the U.S.S.R., a symposium series on Europe between the World Wars opened with lecture by Henry Copeland, a review of a record by Genesis, help for American medical students studying overseas, and medical school programs.https://openworks.wooster.edu/voice1971-1980/1184/thumbnail.jp

    Sensing physical fields: Inverse problems for the diffusion equation and beyond

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    Due to significant advances made over the last few decades in the areas of (wireless) networking, communications and microprocessor fabrication, the use of sensor networks to observe physical phenomena is rapidly becoming commonplace. Over this period, many aspects of sensor networks have been explored, yet a thorough understanding of how to analyse and process the vast amounts of sensor data collected, remains an open area of research. This work therefore, aims to provide theoretical, as well as practical, advances this area. In particular, we consider the problem of inferring certain underlying properties of the monitored phenomena, from our sensor measurements. Within mathematics, this is commonly formulated as an inverse problem; whereas in signal processing it appears as a (multidimensional) sampling and reconstruction problem. Indeed it is well known that inverse problems are notoriously ill-posed and very demanding to solve; meanwhile viewing it as the latter also presents several technical challenges. In particular, the monitored field is usually nonbandlimited, the sensor placement is typically non-regular and the space-time dimensions of the field are generally non-homogeneous. Furthermore, although sensor production is a very advanced domain, it is near impossible and/or extremely costly to design sensors with no measurement noise. These challenges therefore motivate the need for a stable, noise robust, yet simple sampling theory for the problem at hand. In our work, we narrow the gap between the domains of inverse problems and modern sampling theory, and in so doing, extend existing results by introducing a framework for solving the inverse source problems for a class of some well-known physical phenomena. Some examples include: the reconstruction of plume sources, thermal monitoring of multi-core processors and acoustic source estimation, to name a few. We assume these phenomena and their sources can be described using partial differential equation (PDE) and parametric source models, respectively. Under this assumption, we obtain a well-posed inverse problem. Initially, we consider a phenomena governed by the two-dimensional diffusion equation -- i.e. 2-D diffusion fields, and assume that we have access to its continuous field measurements. In this setup, we derive novel exact closed-form inverse formulae that solve the inverse diffusion source problem, for a class of localized and non-localized source models. In our derivation, we prove that a particular 1-D sequence of, so called, generalized measurements of the field is governed by a power-sum series, hence it can be efficiently solved using existing algebraic methods such as Prony's method. Next, we show how to obtain these generalized measurements, by using Green's second identity to combine the continuous diffusion field with a family of well-chosen sensing functions. From these new inverse formulae, we therefore develop novel noise robust centralized and distributed reconstruction methods for diffusion fields. Specifically, we extend these inverse formulae to centralized sensor networks using numerical quadrature; conversely for distributed networks, we propose a new physics-driven consensus scheme to approximate the generalized measurements through localized interactions between the sensor nodes. Finally we provide numerical results using both synthetic and real data to validate the proposed algorithms. Given the insights gained, we eventually turn to the more general problem. That is, the two- and three-dimensional inverse source problems for any linear PDE with constant coefficients. Extending the previous framework, we solve the new class of inverse problems by establishing an otherwise subtle link with modern sampling theory. We achieved this by showing that, the desired generalized measurements can be computed by taking linear weighted-sums of the sensor measurements. The advantage of this is two-fold. First, we obtain a more flexible framework that permits the use of more general sensing functions, this freedom is important for solving the 3-D problem. Second, and remarkably, we are able to analyse many more physical phenomena beyond diffusion fields. We prove that computing the proper sequence of generalized measurements for any such field, via linear sums, reduces to approximating (a family of) exponentials with translates of a particular prototype function. We show that this prototype function depends on the Green's function of the field, and then derive an explicit formula to evaluate the proper weights. Furthermore, since we now have more freedom in selecting the sensing functions, we discuss how to make the correct choice whilst emphasizing how to retrieve the unknown source parameters from the resulting (multidimensional) Prony-like systems. Based on this new theory we develop practical, noise robust, sensor network strategies for solving the inverse source problem, and then present numerical simulation results to verify the performance of our proposed schemes.Open Acces

    Distributed averaging on digital erasure networks

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    International audience; Iterative distributed algorithms are studied for computing arithmetic averages over networks of agents connected through memoryless broadcast erasure channels. These algorithms do not require the agents to have any knowledge about the global network structure or size. Almost sure convergence to state agreement is proved, and the communication and computational complexities of the algorithms are analyzed. Both the number of transmissions and the number of computations performed by each agent of the network are shown to grow not faster than poly-logarithmically in the desired precision. The impact of the graph topology on the algorithms' performance is analyzed as well. Moreover, it is shown how, in the presence of noiseless communication feedback, one can modify the algorithms, significantly improving their performance versus complexity trade-off
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