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Motivation-Based Autonomous Behavior Control of Robotic Computer

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7628)

Abstract

Successful development of a robotic computer as a mediator in smart environments requires providing a certain level of behavior autonomy to the robot and a capability to adapt its behavior in long-term interaction with the users. We attempt to identify core autonomy-related functionalities and describe the design and implementation of an autonomous behavior control subsystem that provides them. The Motivation Module is essential for providing a balance between the robot’s autonomy and our ability to influence its behavior development in a long term. We present the results of two test scenarios illustrating basic use of the newly provided functionality.

Keywords

  • autonomous behavior
  • motivation
  • robotic mediator

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References

  1. Kim, H., Suh, Y.-H., Lee, K., Vladimirov, B.: Introduction to system architecture for a robotic computer. In: Int. Conf. Ubiquitous Robots and Ambient Intelligence (URAI), pp. 607–611 (November 2011)

    Google Scholar 

  2. Froese, T., Virgo, N., Izquierdo, E.: Autonomy: a review and a reappraisal. In: Proc. of 9th European Conference on Artificial Life, Berlin, Germany (2007)

    Google Scholar 

  3. Huang, H., Pavek, K., Novak, B., Albus, J., Messin, E.: A framework for autonomy levels for unmanned systems (ALFUS). In: Proc. of AUVSI’s Unmanned Systems North America, Baltimore, Maryland (2005)

    Google Scholar 

  4. Vernon, D.: Reconciling autonomy with utility: A roadmap and architecture for cognitive development. In: Proc. of Int. Conf. on Biologically-Inspired Cognitive Architectures, pp. 412–418. IOS Press (2011)

    Google Scholar 

  5. Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino, M., Yoshida, C.: Cognitive developmental robotics: A survey. IEEE Trans. Autonomous Mental Development 1(1), 12–34 (2009)

    CrossRef  Google Scholar 

  6. Vernon, D., von Hofsten, C., Fadiga, L.: A Roadmap for Cognitive Development in Humanoid Robots. Cognitive Systems Monographs (COSMOS), vol. 11. Springer (2011)

    Google Scholar 

  7. Vernon, D., Metta, G., Sandini, G.: A survey of artificial cognitive systems: Implications for the autonomous development of mental capabilities in computational agents. IEEE Trans. Evolutionary Computation 11(2), 151–180 (2007)

    CrossRef  Google Scholar 

  8. Sun, R.: The importance of cognitive architectures: An analysis based on CLARION. J. Experimental and Theoretical Artificial Intelligence 19(2), 159–193 (2007)

    CrossRef  Google Scholar 

  9. Tenorth, M., Beetz, M.: KnowRob – Knowledge Processing for Autonomous Personal Robots. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 4261–4266 (2009)

    Google Scholar 

  10. Duch, W., Setiono, R., Zurada, J.: Computational intelligence methods for rule-based data understanding. Proc. IEEE 92, 771–805 (2004)

    CrossRef  Google Scholar 

  11. Rummery, G.A., Niranjan, M.: On-line Q-learning using connectionist systems. Technical Report CUED/F-INFENG/TR 166, University of Cambridge, Cambridge, England (1994)

    Google Scholar 

  12. Koo, S.-Y., Park, K., Kwon, D.-S.: A dual-layer user model based cognitive system for user-adaptive service robots. In: 20th IEEE International Symposium on Robot and Human Interactive Communication (Ro-Man 2011), pp. 59–64 (2011)

    Google Scholar 

  13. Suh, Y.-H., Lee, K.-W., Lee, M., Kin, H., Cho, E.-S.: ICARS: Integrated Control Architecture for the Robotic mediator in Smart environments A Software Framework for the Robotic Mediator collaborating with Smart Environments. In: 9th IEEE International Conference on Embedded Software and Systems (ICESS 2012), pp. 25–27 (2012)

    Google Scholar 

  14. Fogg, B.J.: Persuasive Technology: Using Computers to Change What We Think and Do. Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

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© 2012 Springer-Verlag Berlin Heidelberg

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Vladimirov, B., Kim, H., Park, N. (2012). Motivation-Based Autonomous Behavior Control of Robotic Computer. In: Noda, I., Ando, N., Brugali, D., Kuffner, J.J. (eds) Simulation, Modeling, and Programming for Autonomous Robots. SIMPAR 2012. Lecture Notes in Computer Science(), vol 7628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34327-8_5

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  • DOI: https://doi.org/10.1007/978-3-642-34327-8_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34326-1

  • Online ISBN: 978-3-642-34327-8

  • eBook Packages: Computer ScienceComputer Science (R0)