A GAME PLAYING ROBOT THAT CAN LEARN A TACTICAL KNOWLEDGE THROUGH INTERACTING WITH A HUMAN

Raafat Mahmoud, Atsushi Ueno, Shoji Tatsumi

Abstract

We propose a new approach for teaching a humanoid-robot a task online without pre-set data provided in advance. In our approach, human acts as a collaborator and also as a teacher. The proposed approach enables the humanoid-robot to learn a task through multi-component interactive architecture. The components are designed with the respect to human methodology for learning a task through empirical interactions. For efficient performance, the components are isolated within one single API. Our approach can be divided into five main roles: perception, representation, state/knowledge-up-dating, decision making and expression. A conducted empirical experiment for the proposed approach is to be done by teaching a Fujitsu’s humanoid-robot "Hoap-3" an X-O game strategy and its results are to be done and explained. Important component such as observation, structured interview, knowledge integration and decision making are described for teaching the robot the game strategy while conducting the experiment.

References

  1. Sweller, J., 2006. Visualization and instructional design. In3rd Australasian conference on Interactive entertainment, Vol, 207,pp 91-95.
  2. Bransford, J., Brown, A., & Cocking, R, 2001. How people learn. Brain, Mind, Experience, and School. Expanded version. National Academy Press: Washington, DC. p. 33.
  3. Baddeley, A. D., 1996. Human Memory: Theory and Practice. Hove: Psychology Press.
  4. Marois, R. 2005. Two-timing attention. Nature Neuroscience. In Nature Neuroscience. pp 1285-1286.
  5. Kuniyoshi. Y., Inaba M., and Inoue H. 1994. Learning by watching: Extracting reusable task knowledge from visual observation of human performance. In IEEE Transactions on Robotics and Automation, vol 10 pp.:799-822.
  6. Voyles. R and Khosla P., 1998. A multi-agent system for programming robotic agents by human demonstration. In Proceedings of AI and Manufacturing Research Planning Workshop.
  7. Reeves, B. & Nass, C. 1996. The media equation-how people treat computers, television, and new media like real people and places. Cambridge, UK: Cambridge University Press.
  8. Lockerd A., Breazeal C. 2004 Tutelage and socially guided robot learning”. In Proceedings. International Conference on Intelligent Robots and Systems, IEEE/RSJ Vol. 4, pp. 3475 - 3480..
  9. Nicolescu M. N., Mataric M. J., 2001. Learning and interacting in human-robot domains” In Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on Vol. 31 No.5. ,pp. 419 - 430.
  10. Barry Brian Werger, 2000. Ayllu: Distributed portarbitrated behaviour-based control. In Proc., The 5th Intl. Symp. On Distributed Autonomous Robotic Systems, Knoxville, TN, pp. 25-34
  11. Maes P. and Brooks R. A. 1990. Learning to coordinate behaviours. In Pros AAAI, Boston, MA, pp.796-802.
  12. Mahadevan S. and Connell J., 1991. Scaling reinforcement learning to robotics by exploiting the subsumption architecture. In Proc. Eighth Int. Workshop Machine Learning, pp.328-337.
  13. Lauria S., Bugmann G., 2002 Mobile robot programming using natural language. Robotics and Autonomous Systems, 38(3-4):171-181.
  14. Brian S, Gonzalez J. 2008. Discovery of High-Level Behaviour from Observation of Human Performance in a Strategic Game. In Systems and Humans, IEEE Transactions on Vol. 38 No.3, pp. 855 - 874.
  15. Mahnmoud, R. A., Ueno, A., Tatsumi S., 2008, A game Playing robot that can learn from experience. In HSI'08 on Human System Interactions pp. 440-445.
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Paper Citation


in Harvard Style

Mahmoud R., Ueno A. and Tatsumi S. (2011). A GAME PLAYING ROBOT THAT CAN LEARN A TACTICAL KNOWLEDGE THROUGH INTERACTING WITH A HUMAN . In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-40-9, pages 609-616. DOI: 10.5220/0003182406090616


in Bibtex Style

@conference{icaart11,
author={Raafat Mahmoud and Atsushi Ueno and Shoji Tatsumi},
title={A GAME PLAYING ROBOT THAT CAN LEARN A TACTICAL KNOWLEDGE THROUGH INTERACTING WITH A HUMAN},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2011},
pages={609-616},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003182406090616},
isbn={978-989-8425-40-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - A GAME PLAYING ROBOT THAT CAN LEARN A TACTICAL KNOWLEDGE THROUGH INTERACTING WITH A HUMAN
SN - 978-989-8425-40-9
AU - Mahmoud R.
AU - Ueno A.
AU - Tatsumi S.
PY - 2011
SP - 609
EP - 616
DO - 10.5220/0003182406090616