skip to main content
10.1145/2016039.2016064acmconferencesArticle/Chapter ViewAbstractPublication Pagesacm-seConference Proceedingsconference-collections
research-article

Computer, know thyself: exploring consciousness via self-aware machines

Published:24 March 2011Publication History

ABSTRACT

We describe an implementation of machine self-awareness in which a computer can identify itself among otherwise identical-looking peers based on visual feedback from its own externally observable behavior. Through a combination of both innate and learned mechanisms, the system demonstrates how a machine can successfully participate in a type of poor-man's mirror test. This work forms a foundation from which to address larger issues of machine consciousness and is predicated on the hypothesis that self-awareness is one of the most fundamental aspects of consciousness and also one of the most tractable. Potential limitations and concerns are discussed.

References

  1. Aleksander, I. 2007. Machine consciousness. In Blackwell Companion to Consciousness, M. Velmans and S. Schneider, Eds. Blackwell, Malden, MA.Google ScholarGoogle Scholar
  2. Baars, B. 1997. In the theater of consciousness. Oxford University Press, New York, NY.Google ScholarGoogle Scholar
  3. Braitenberg, V. 1984. Vehicles. MIT Press, Cambridge, MA.Google ScholarGoogle Scholar
  4. Clark, A. 1997. Being There: Putting Brain, Body, and World Together Again. MIT Press, Cambridge, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Dennett, D. 1996. Kinds of minds. Basic, New York, NY.Google ScholarGoogle Scholar
  6. Donaldson, S. 2008. A neural network for creative serial order cognitive behavior. Minds and Machines, 18:53--91. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Edelman, G. 1992. Bright air, brilliant fire. Basic, New York, NY.Google ScholarGoogle Scholar
  8. Elman, J. 1990. Finding structure in time. Cognitive Science, 14, 179--211.Google ScholarGoogle ScholarCross RefCross Ref
  9. Gallup, G. 1970. Chimpanzees: Self-recognition. Science, 167 (3914) 86--87.Google ScholarGoogle Scholar
  10. Heilman, K., 1991. Anosognosia: Possible Neuropsychological Mechanisms. In Awareness of Deficit After Brain Injury, G. Prigatano and D. Schacter, Eds. Oxford University Press, Oxford, UK.Google ScholarGoogle Scholar
  11. Hofstadter, D. 1985. Metamagical themas: Questing for the Essence of Mind and Pattern. Basic, New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Hofstadter, D. 1995. Fluid concepts and creative analogies. Basic, New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Hofstadter, D. 2007. I am a Strange Loop. Basic, New York, NY.Google ScholarGoogle Scholar
  14. Jackendoff, R. 1987. Consciousness and the computational mind. MIT Press, Cambridge, MA.Google ScholarGoogle Scholar
  15. Jaynes, J. 1976. The origin of consciousness in the breakdown of the bicameral mind. Houghton Mifflin, Boston, MA.Google ScholarGoogle Scholar
  16. Kandel, E. & Kupfermann, I. 1995. From nerve cells to cognition. In Essentials of neural science and behavior, E. Kandel, J. Schwartz, and T. Jessell, Eds. Appleton & Lange, Norwalk, CT.Google ScholarGoogle Scholar
  17. Kanerva, P. 1988. Sparse distributed memory. MIT Press, Cambridge, MA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Michel, P., Gold, K., Scassellati, B. 2004. Motion-based robotic self-recognition. Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2763--2768.Google ScholarGoogle ScholarCross RefCross Ref
  19. Olshausen, B., Anderson, C., and Van Essen, D. 1993. A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. The Journal of Neuroscience, 13(11): 4700--4719.Google ScholarGoogle ScholarCross RefCross Ref
  20. Penrose, R. 1989. The emperor's new mind. Oxford University Press, Oxford, UK.Google ScholarGoogle Scholar
  21. Rich, E., & Knight, K. 1991. Artificial intelligence. McGraw-Hill, New York, NY. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Wang, L., and Alkon, D. 1993. Temporal processing with a biologically based artificial network. In Artificial Neural Networks: Oscillations, Chaos, and Sequence Processing, L. Wang & D. Alkon, Eds. IEEE Computer Society Press, Los Alamitos, CA.Google ScholarGoogle Scholar

Index Terms

  1. Computer, know thyself: exploring consciousness via self-aware machines

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        ACM-SE '11: Proceedings of the 49th Annual Southeast Regional Conference
        March 2011
        399 pages
        ISBN:9781450306867
        DOI:10.1145/2016039

        Copyright © 2011 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 March 2011

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate134of240submissions,56%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader