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8      L.C. Jain et al.
                           machine to learn from its changing environment and to adapt to the new
                           circumstances is discussed. Although there are various machine intelligence
                           techniques to impart learning to machines, it is yet to have a universal one for
                           this purpose. Some applications of intelligent machines are highlighted, which
                           include unmanned aerial vehicles, underwater robots, space vehicles, and
                           humanoid robots, as well as other projects in realizing intelligent machines.
                           It is anticipated that intelligent machines will ultimately play a role, in one
                           way or another, in our daily activities, and make our life comfortable in future.


                           References

                            1. “Mainstream Science on Intelligence”, Wall Street Journal, Dec. 13, 1994, p A18.
                            2. “Artificial Intelligence”, Encyclopædia Britannica. 2007. Encyclopædia Britan-
                               nica Online, <http://www.britannica.com/eb/article-9009711>, access date: 10
                               Feb 2007
                            3. S. Takamuku and R.C. Arkin, “Multi-method Learning and Assimilation”,
                               Mobile Robot Laboratory Online Publications, Georgia Institute of Technology,
                               2007.
                            4. S.C. Shapiro, Artificial Intelligence, in A. Ralston, E.D. Reilly, and D. Hem-
                               mendigner, Eds. Encyclopedia of Computer Science, Fourth Edition,. New York
                               Van Nostrand Reinhold, 1991
                            5. S. Schaal, “Is imitation learning the route to humanoid robots?” Trends in
                               Cognitive Scienes, vol. 3, pp. 233–242, 1999.
                            6. J. Peters, S. Vijayakumar, and S. Schaal, “Reinforcement learning for humanoid
                               robotics”, Proceedings of the third IEEE-RAS International Conference on
                               Humanoid Robots, 2003.
                            7. S. Patnaik, L. Jain, S. Tzafestas, G. Resconi, and A. Konar, (eds), Innovations
                               in Robot Mobility and Control, Springer, 2006.
                            8. B. Apolloni, A. Ghosh, F. Alpaslan, L. Jain, and S. Patnaik, (eds), Machine
                               Learning and Robot Perception, Springer, 2006.
                            9. L.C. Jain, and T. Fukuda, (editors), Soft Computing for Intelligent Robotic
                               Systems, Springer-Verlag, Germany, 1998.
                           10. P. Langley, “Machine learning for intelligent systems,” Proceedings of Fourteenth
                               National Conference on Artificial Intelligence, pp. 763–769, 1997.
                           11. F. Sahin and J.S. Bay, “Learning from experience using a decision-theoretic
                               intelligent agent in multi-agent systems”, Proceedings of the 2001 IEEE Moun-
                               tain Workshop on Soft Computing in Industrial Applications, pp. 109–114, 2001.
                           12. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible
                               Inference, Morgan Kaufmann, 1988.
                           13. D.A. Schoenwald, “AUVs: In space, air, water, and on the ground”, IEEE Con-
                               trol Systems Magazine, vol. 20, pp. 15–18, 2000.
                           14. A. Ryan, M. Zennaro, A. Howell, R. Sengupta, and J.K. Hedrick, “An overview
                                                                                    rd
                               of emerging results in cooperative UAV control”, Proceedings of 43 IEEE Con-
                               ference on Decision and Control, vol. 1, pp. 602–607, 2004.
                           15. “NOAA Missions Now Use Unmanned Aircraft Systems”, NOAA Mag-
                               azine Online (Story 193), 2006, <http://www.magazine.noaa.gov/stories/
                               mag193.htm>, access date: 13 Feb, 2007
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