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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 Erscheinungsdatum

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  • 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
    News
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012)
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  • 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.
  • 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.
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    Towards Computational Cognitive Modeling of Mental Imagery
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Sima, Jan Frederik; Freksa, Christian
    Mental imagery is the human ability to imagine and reason with visuo-spatial information. It is crucial for everyday tasks such as describing a route or remembering the form of objects. The so-called imagery debate has been centered around the question how mental imagery is realized, i.e., what structures and algorithms can plausibly explain and model mental imagery. There is, however, little progress on a coherent theory that can sufficiently cover the diversity of the empirical data. This article presents a new theory of mental imagery, which in contrast to other contemporary theories is formalized as a computational cognitive model. We will compare this theory to the contemporary theories using two representative phenomena of mental imagery. We will argue that formalized theories can advance the currently stagnant imagery debate.
  • Zeitschriftenartikel
    Neue Medien? – Neue Uni!
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Furbach, Ulrich
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    Revealing Underlying Structure and Behaviour from Movement Data
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Sester, Monika; Feuerhake, Udo; Kuntzsch, Colin; Zhang, Lijuan
    Spatio-temporal trajectories contain implicit knowledge about the movement of individuals, which is relevant for problems in various domains, e.g. animal migration, traffic analysis, security. In this paper we present real-time approaches to segment trajectories into meaningful parts which reflect the underlying typical behaviour or structure. Based on this information atypical behaviour can be identified.
  • Zeitschriftenartikel
    Die Freiburger „Stanford AI Class“ Erfahrung
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Burgard, Wolfram
  • Zeitschriftenartikel
    Learning ∈ Artificial Intelligence ∩ Cognitive Technologies ∩ Neural Computation ∩ …
    (KI - Künstliche Intelligenz: Vol. 26, No. 3, 2012) Igel, Christian