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Künstliche Intelligenz 26(4) - November 2012

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  • Zeitschriftenartikel
    Adaptive Dissimilarity Measures, Dimension Reduction and Visualization (University of Groningen)
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Bunte, Kerstin
  • Zeitschriftenartikel
    Neural Networks for Complex Data
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Cottrell, Marie; Olteanu, Madalina; Rossi, Fabrice; Rynkiewicz, Joseph; Villa-Vialaneix, Nathalie
    Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such as graphs and functions. This paper summarizes advances on those themes from the last decade, with a focus on results obtained by members of the SAMM team of Université Paris 1.
  • Zeitschriftenartikel
    Connecting Question Answering and Conversational Agents
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Waltinger, Ulli; Breuing, Alexa; Wachsmuth, Ipke
    Research results in the field of Question Answering (QA) have shown that the classification of natural language questions significantly contributes to the accuracy of the generated answers. In this paper we present an approach which extends the prevalent question classification techniques by additionally considering further contextual information provided by the questions. Thereby we focus on improving the conversational abilities of existing interactive interfaces by enhancing their underlying QA systems in terms of response time and correctness. As a result, we are able to introduce a method based on a tripartite contextualization. First, we present a comprehensive question classification experiment based on machine learning using two different datasets and various feature sets for the German language. Second, we propose a method for detecting the focus chunk of a given question, that is, for identifying which part of the question is fundamentally relevant to the answer and which part refers to a specification of it. Third, we investigate how to identify and label the topic of a given question by means of a human-judgment experiment. We show that the resulting contextualization method contributes to an improvement of existing question answering systems and enhances their application within interactive scenarios.
  • Zeitschriftenartikel
    Challenges in Neural Computation
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Hammer, Barbara
    This contribution contains a short history of neural computation and an overview about the major learning paradigms and neural architectures used today.
  • Zeitschriftenartikel
    Sparse Coding and Selected Applications
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Hocke, Jens; Labusch, Kai; Barth, Erhardt; Martinetz, Thomas
    Sparse coding has become a widely used framework in signal processing and pattern recognition. After a motivation of the principle of sparse coding we show the relation to Vector Quantization and Neural Gas and describe how this relation can be used to generalize Neural Gas to successfully learn sparse coding dictionaries. We explore applications of sparse coding to image-feature extraction, image reconstruction and deconvolution, and blind source separation.
  • Zeitschriftenartikel
    Recurrent Neural Networks for Industrial Procurement Decisions
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Zimmermann, Hans-Georg; Tietz, Christoph; Grothmann, Ralph; Runkler, Thomas
    Rational decisions are based upon forecasts. Precise forecasting has therefore a central role in business. The prediction of commodity prices or the prediction of energy load curves are prime examples. We introduce recurrent neural networks to model economic or industrial dynamic systems.
  • Zeitschriftenartikel
    Search and Topic Detection in Customer Requests
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Eichler, Kathrin; Meisdrock, Matthias; Schmeier, Sven
    Customer support departments of large companies are often faced with large amounts of customer requests about the same issue. These requests are usually answered by using preformulated text blocks. However, choosing the right text from a large number of text blocks can be challenging for the customer support agent, especially when the text blocks are thematically related. Optimizing this process using the power of language and knowledge technologies can save resources and improve customer satisfaction. We present a joint project between OMQ GmbH (www.omq.de) and the Language Technology lab of the DFKI GmbH (www.dfki.de) (German Research Center for Artificial Intelligence), in which, starting from the customer support system developed by OMQ, we addressed two major challenges: First, the classification of incoming customer requests into previously defined problem cases; second, the identification of new problem cases in a set of unclassified customer requests. The two tasks were approached using linguistic and statistical methods combined with machine learning techniques.
  • Zeitschriftenartikel
    Interview with Helge Ritter
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Mokbel, Bassam
  • Zeitschriftenartikel
    Reservoir Computing with Output Feedback
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Reinhart, René Felix
    This thesis presents a dynamical system approach to learning forward and inverse models in associative recurrent neural networks. Ambiguous inverse models are represented by multi-stable dynamics. Random projection networks, i.e. reservoirs, together with a rigorous regularization methodology enable robust and efficient training of multi-stable dynamics with application to movement control in robotics.
  • Zeitschriftenartikel
    Special Issue on Neural Learning Paradigms
    (KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Hammer, Barbara