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Künstliche Intelligenz 26(3) - August 2012

https://dl.gi.de/handle/20.500.12116/11104
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Auflistung Künstliche Intelligenz 26(3) - August 2012 nach Titel

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  • Zeitschriftenartikel
    Aspects of Universitarian Distance Teaching and Online Learning
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Beierle, Christoph; Kern-Isberner, Gabriele
    Already well before the arrival of the Internet and the world wide web, distance teaching universities were established in various countries. Using the ubiquitous availability of the WWW and advances in modern information and communication technologies, new and exciting online learning offerings like Udacity are now available. From a personal perspective, we discuss aspects of universitarian distance teaching and online learning and present thoughts about benefits the different learning forms may provide.
  • Zeitschriftenartikel
    Data Science for Simulating the Era of Electric Vehicles
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Janssens, Davy; Giannotti, Fosca; Nanni, Mirco; Pedreschi, Dino; Rinzivillo, Salvatore
    The vision and scientific challenges presented in this paper are the objectives of the FET-FP7 project DATASIM. The project aims at providing an entirely new and highly detailed spatio-temporal microsimulation methodology for human mobility, grounded on massive amounts of big data of various types and from various sources, with the goal to forecast the nation-wide consequences of a massive switch to electric vehicles, given the intertwined nature of mobility and power distribution networks.
  • Zeitschriftenartikel
    Die Freiburger „Stanford AI Class“ Erfahrung
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Burgard, Wolfram
  • Zeitschriftenartikel
    Discovering the Geographical Borders of Human Mobility
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Rinzivillo, Salvatore; Mainardi, Simone; Pezzoni, Fabio; Coscia, Michele; Pedreschi, Dino; Giannotti, Fosca
    The availability of massive network and mobility data from diverse domains has fostered the analysis of human behavior and interactions. Broad, extensive, and multidisciplinary research has been devoted to the extraction of non-trivial knowledge from this novel form of data. We propose a general method to determine the influence of social and mobility behavior over a specific geographical area in order to evaluate to what extent the current administrative borders represent the real basin of human movement. We build a network representation of human movement starting with vehicle GPS tracks and extract relevant clusters, which are then mapped back onto the territory, finding a good match with the existing administrative borders. The novelty of our approach is the focus on a detailed spatial resolution, we map emerging borders in terms of individual municipalities, rather than macro regional or national areas. We present a series of experiments to illustrate and evaluate the effectiveness of our approach.
  • Zeitschriftenartikel
    Efficient Traffic Density Prediction in Road Networks Using Suffix Trees
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Kriegel, Hans-Peter; Renz, Matthias; Schubert, Matthias; Züfle, Andreas
    Recently, modern tracking methods started to allow capturing the position of massive numbers of moving objects. Given this information, it is possible to analyze and predict the traffic density in a network which offers valuable information for traffic control, congestion prediction and prevention. In this paper, we propose a statistical approach to predict the density on any edge in such a network at a future point of time. Our method combines long-term and short-term observations of a traffic network in order to predict traffic density for the near future. In our experiments, we show the capability of our approach to make useful predictions about the traffic density and illustrate the efficiency of our new algorithm when calculating these predictions.
  • Zeitschriftenartikel
    Erfahrungsbericht zum Online KI-Kurs von Sebastian Thrun und Peter Norwig
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Fleckenstein, Freya
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  • Zeitschriftenartikel
    Interview with Josep Domingo-Ferrer and Chris Clifton on Privacy in Spatiotemporal Data Analysis
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Körner, Christine
  • Zeitschriftenartikel
    Interview with Yücel Saygin on Privacy in Spatiotemporal Data Analysis
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Körner, Christine
  • Zeitschriftenartikel
    Learning and Self-organization for Spatiotemporal Systems
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Runkler, Thomas A.; Sollacher, Rudolf; Szabo, Andrei
    This article deals with the modeling and management of spatiotemporal systems using machine learning and self-organization algorithms. Two application examples are the localization of objects from radio measurements using spatiotemporal models learned from data, and the self-organizing management of wireless multi-hop sensor networks. For both examples we show how machine learning and self-organization significantly increases accuracy and efficiency.