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  • × author_ss:"Schank, R.C."
  1. Schank, R.C.; Childers, P.G.: ¬Die Zukunft der künstlichen Intelligenz : Chancen und Risiken (1986) 0.01
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    Abstract
    Dieses Buch handelt vom Wesen der menschlichen Intelligenz und davon, was es bedeuten würde, über 'Maschinen-Intelligenz' zu verfügen. Schon immer war es Ziel und Wunschtraum genialer Denker und Erfinder, die 'denkende Maschine' als Hilfsapparat für den Menschen zu konstruieren. Erst in unserer Zeit nähern wir uns mit Unterstützung des Computers der Verwirklichung der in der Tat hochfliegenden Pläne. Die Öffentlichkeit hat die Künstliche Intelligenz entdeckt, ist aber nicht ganz sicher, was das eigentlich ist. Schließlich müssen wir die Frage, was Computer können, parallel zu der Frage betrachten, WAS MENSCHEN KÖNNEN. Es fällt daher bis heute selbst den Fachleuten außerordentlich schwer, den Computer-Maschinen das Denken beizubringen.
    BK
    54.08 Informatik in Beziehung zu Mensch und Gesellschaft
    Classification
    54.08 Informatik in Beziehung zu Mensch und Gesellschaft
  2. Schank, R.C.: Computer, elementare Aktionen und linguistische Theorien (1977) 0.00
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    Series
    Grundlagen der Kommunikation und Kognition
    Source
    Semantik und künstliche Intelligenz: Beiträge zur automatischen Sprachbearbeitung II. Hrsg. und eingeleitet von P. Eisenberg
  3. Schank, R.C.; Fano, A.: Memory and expectations in learning, language, and visual understanding (1995) 0.00
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    Abstract
    Research in vision and language has traditionally remained separate, in part because the classic task of generating a representation of a given image or sentence has resulted in an emphasis on low lewel structural aspects of these media. Image and language understanding should be approaches with the intent of facilitating the performance of a task. Under this view research in image and language understanding must confront common issues that arise as a task is pursued. Languages and images are both input that can be used to maintain a model of a task. A model may by maintained by incorporating changes in the scene that can be characterizes at a high level of analysis. Existing task rlevant models and the associated domain knowledge are used to expect specific changes and disambiguate the interpretation of these changes, thereby allowing them to modify the existing model. From this perspective, understanding input is largely independent of the modality of the input