Alle Publikationen
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2017
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(2017): Type and Metaphor for Computer Programmers. In: Techné: Research in Philosophy and Technology 21 (1), S. 71-105. DOI: 10.5840/techne20174662
Abstract: The duality of computer programs is characterized, on the one hand, by their physical implementations on physical devices, and, on the other, by the conceptual implementations in programmers’ minds of the objects making up the computational processes they conceive. We contend that central to programmers’ conceptual implementations are (i) the concept of type, at both the programming and the design level, and (ii) metaphors created to facilitate these implementations.
2016
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(2016) : Modularization of skill ontologies for industrial robots In: VDE: Proceedings of ISR 2016: 47st International Symposium on Robotics: Berlin: VDE Verlag GmbH, S. 1-6. Online verfügbar unter https://ieeexplore.ieee.org/document/7559114/
Abstract: With industrial robots ready to take the next step in mastering manufacturing tasks new approaches to reduce the programming effort are needed. This is achieved by introducing skills as robot "know-how" and using them as a higher abstraction level of robot instructions during programming. The skills are reusable items providing motion control and rich declarative descriptions of complex robot capabilities. Storing the skills requires an adequate knowledge representation model that enables reuse and reasoning on skills and simplifies knowledge management. In this paper we report on development of a skill representation model and its implementation in a knowledge base. The developed model is effectively a class hierarchy of the skill concepts implemented in a modularized ontology structure. The resulting model clarifies the intrinsic concepts of a skill and presents a module structure that enables the future development and reuse of skills in general.
1997
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(1997): Long Short-Term Memory. In: Neural Computation 9 (8), S. 1735-1780. DOI: 10.1162/neco.1997.9.8.1735
DOI: https://doi.org/10.1162/neco.1997.9.8.1735 Abstract: Learning to store information over extended time intervals via recurrent backpropagation takes a very long time, mostly due to insucient, decaying error back ow. We brie y review Hochreiter's 1991 analysis of this problem, then address it by introducing a novel, ecient, gradient-based method called \Long Short-Term Memory" (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete time steps by enforcing constant error ow through \constant error carrousels" within special units. Multiplicative gate units learn to open and close access to the constant error ow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with articial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with RTRL, BPTT, Recurrent Cascade-Correlation, Elman nets, and Neural Sequence Chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, articial long time lag tasks that have never been solved by previous recurrent network algorithms.
Keywords: deutsche Community, Intellektualtechnik, Kogn. Architektur, kognitive Architekturen, Künstliche Intelligenz, Mensch-Technik-Relationen (MTR), Neuronale Netzwerke, Programmieren, Realtechnik, Sprachverstehen, Technik, Wiedererkennen / Erinnerung / Erkennen von Personen und Sitautionen, Social Recognition
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