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Auflistung Künstliche Intelligenz 26(4) - November 2012 nach Erscheinungsdatum
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- ZeitschriftenartikelSpecial Issue on Neural Learning Paradigms(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Hammer, Barbara
- ZeitschriftenartikelSlow Feature Analysis: Perspectives for Technical Applications of a Versatile Learning Algorithm(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Escalante-B., Alberto N.; Wiskott, LaurenzSlow Feature Analysis (SFA) is an unsupervised learning algorithm based on the slowness principle and has originally been developed to learn invariances in a model of the primate visual system. Although developed for computational neuroscience, SFA has turned out to be a versatile algorithm also for technical applications since it can be used for feature extraction, dimensionality reduction, and invariance learning. With minor adaptations SFA can also be applied to supervised learning problems such as classification and regression. In this work, we review several illustrative examples of possible applications including the estimation of driving forces, nonlinear blind source separation, traffic sign recognition, and face processing.
- ZeitschriftenartikelNeural Learning of Cognitive Control(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Hamker, Fred H.Our goal is to develop cognitive agents based on neuroscientific evidence. The efficiency of cognitive behavior depends on its capacity to select, represent and manipulate sufficient knowledge of the environment to achieve its goals. We designed a biologically motivated model of basal ganglia and particularly the prefrontal cortex and here review its foundations of neural learning and summarize our obtained results.
- ZeitschriftenartikelSearch and Topic Detection in Customer Requests(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Eichler, Kathrin; Meisdrock, Matthias; Schmeier, SvenCustomer 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.
- ZeitschriftenartikelInterview with Helge Ritter(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Mokbel, Bassam
- ZeitschriftenartikelReservoir Computing with Output Feedback(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Reinhart, René FelixThis 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.
- ZeitschriftenartikelReservoir Computing Trends(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Lukoševičius, Mantas; Jaeger, Herbert; Schrauwen, BenjaminReservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the readouts from it. It started ten years ago and is currently a prolific research area, giving important insights into RNNs, practical machine learning tools, as well as enabling computation with non-conventional hardware. Here we give a brief introduction into basic concepts, methods, insights, current developments, and highlight some applications of RC.
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012)
- ZeitschriftenartikelHow Rich Motor Skills Empower Robots at Last: Insights and Progress of the AMARSi Project(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Soltoggio, Andrea; Steil, Jochen J.Flexible, robust, precise, adaptive, compliant and safe: these are some of the qualities robots must have to interact safely and productively with humans. Yet robots are still nowadays perceived as too rigid, clumsy and not sufficiently adaptive to work efficiently in interaction with people. The AMARSi Project endeavors to design and implement rich motor skills, unique flexibility, compliance and state-of-the-art learning in robots. Inspired by human-recorded motion and learning behavior, similarly versatile and constantly adaptive movements and skills endow robots with singularly human-like motor dynamics and learning. The AMARSi challenge is to integrate novel biological notions, advanced learning algorithms and cutting-edge compliant mechanics in the design of fully-fledged humanoid and quadruped robots with an unprecedented aptitude for integrating in our environments.
- ZeitschriftenartikelRecurrent Neural Networks for Industrial Procurement Decisions(KI - Künstliche Intelligenz: Vol. 26, No. 4, 2012) Zimmermann, Hans-Georg; Tietz, Christoph; Grothmann, Ralph; Runkler, ThomasRational 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.