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Knowledge representation for robotic vision based on conceptual spaces and attentive mechanisms

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 992))

Abstract

A new cognitive architecture for artificial vision is proposed. The architecture is aimed for an autonomous intelligent system, as several cognitive hypotheses have been postulated as guidelines for its design. The design is based on a conceptual representation level between the subsymbolic level processing the sensory data, and the linguistic level describing scenes by means of a high-level language. The architecture is also based on the active role of a focus of attention mechanism in the link between the conceptual and the linguistic level. The link between the conceptual level and the linguistic level is modelled as a time-delay attractor neural network.

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References

  1. R.J. Brachman, J.C. Schmoltze: An Overview of the KL-ONE Knowledge Representation System, Cognitive Science, 9, 2, 171–216, 1985.

    Google Scholar 

  2. A. Chella, M. Frixione, S. Gaglio: A Hybrid Model for Visual Perception Based on Dynamic Conceptual Space, Proc. ECAI-94 Workshop on Combining Symbolic and Connectionist Processing, 123–132, Amsterdam, The Netherlands, 1994.

    Google Scholar 

  3. D. Davidson: The Logical Form of Action Sentences, in: N. Rescher (ed.): The Logic of Decision and Action, 81–95, University of Pittsburgh Press, Pittsburgh, Pa. 1967.

    Google Scholar 

  4. P. Gärdenfors: Three Levels of Inductive Interence, Lund University Cognitive Studies 9, Tech. Rep. LUHFDA/HFKO-5006-SE, 1992.

    Google Scholar 

  5. J.J. Hopfield: Neural Networks and Physical Systems with Emergent Collective Computational Abilities, Proceedings of the National Academy of Sciences, USA, 79, 2554–2558, 1982.

    Google Scholar 

  6. D. Kleinfeld: Sequential State Generation by Model Neural Networks, Proc. Nat. Acad. Sci. USA 83, 9469–9473, 1986.

    Google Scholar 

  7. B. Nebel: Reasoning and Revision in Hybrid Representation Systems, LNAI 422, Springer-Verlag, Berlin, 1990.

    Google Scholar 

  8. A.P. Pentland: Perceptual Organization and the Representation of Natural Form, Artificial Intelligence, 28, 293–331, 1986.

    Google Scholar 

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Marco Gori Giovanni Soda

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© 1995 Springer-Verlag Berlin Heidelberg

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Chella, A., Frixione, M., Gaglio, S. (1995). Knowledge representation for robotic vision based on conceptual spaces and attentive mechanisms. In: Gori, M., Soda, G. (eds) Topics in Artificial Intelligence. AI*IA 1995. Lecture Notes in Computer Science, vol 992. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60437-5_28

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  • DOI: https://doi.org/10.1007/3-540-60437-5_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60437-2

  • Online ISBN: 978-3-540-47468-5

  • eBook Packages: Springer Book Archive

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