Industry 4.0
Vol. 5 (2020), Issue 5, pg(s) 210-213

TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”

Teaching artificial intelligence in cyber-physical systems

  • 1 University of Chemical Technology and Metallurgy, Sofia, Bulgaria

Abstract

A training discipline “Basics if the Artificial Intelligence”, taught in UCTM, Sofia is presented. The discipline is developed according to the requirements of ACM as well the methodises presented by Russel and Norvig in their seminal book “Artificial Intelligence: A modern approach, 3 ed.”. As a tool for illustration of the AI algorithms to be studied, each student is equipped with a typical cyberphysical system – individual training stand, which includes a simplified robot (addressed here as passive agent) constructed of sensors and actuators only) controlled by an AI algorithm, subject to development by the students and housed on a PC. An outline of the lections is
included.

Keywords

References

  1. S. Roach and M. Sahami, “ACM and IEEE: Computer Science Curricula 2013,” Computer (Long. Beach. Calif)., 2013, doi: 10.1109/MC.2015.68.
  2. “The ACM Computer Science Curricula 2013.” doi: 10.1145/2534860.
  3. S. Russell and P. Norvig, Artificial intelligence: A Modern Approach, vol. 3. Pearson Education Limited, 2016.
  4. F. Chesani, A. Galassi, P. Mello, and G. Trisolini, “A game-based competition as instrument for teaching artificial intelligence,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10640 LNAI, no. November 2017, pp. 72– 84, 2017, doi: 10.1007/978-3-319-70169-1_6.
  5. M. Tambe, A. Balsamo, and E. Bowring, “Using science fiction in teaching artificial intelligence,” in AAAI Spring Symposium - Technical Report, 2008, vol. SS-08-08, pp. 86–91.
  6. J. C. Burguillo, “Using game theory and Competition-based Learning to stimulate student motivation and performance,” Comput. Educ., vol. 55, no. 2, pp. 566–575, 2010, doi: 10.1016/j.compedu.2010.02.018.
  7. Z. Dodds, L. Greenwald, A. Howard, S. Tejada, and J. Weinberg, “Components, curriculum, and community: Robots and robotics in undergraduate AI education,” AI Mag., vol. 27, no. 1, pp. 11–22, 2006.
  8. A. N. Kumar, “Using robots in an undergraduate artificial intelligence course: An experience report,” Proc. - Front. Educ. Conf., vol. 2, pp. 10–14, 2001, doi: 10.1109/fie.2001.963650.
  9. D. Kumar and L. Meeden, “A robot laboratory for teaching artificial intelligence,” in SIGCSE Bulletin (Association for Computing Machinery, Special Interest Group on Computer Science Education), 1998, vol. 30, no. 1, pp. 341–344, doi: 10.1145/274790.274326.
  10. L. Greenwald and D. Artz, “Teaching artificial intelligence with low-cost robots,” in AAAI Spring Symposium - Technical Report, 2004, vol. 1, pp. 35–40.
  11. L. Greenwald, D. Artz, Y. Mehta, and B. Shirmohammadi, “Using Educational Robotics to Motivate Complete AI Solutions,” vol. 27, no. 1, pp. 83–95, 2006.
  12. S. P. Imberman, “Teaching neural networks using LEGO handy board robots in an artificial intelligence course,” in SIGCSE Bulletin (Association for Computing Machinery, Special Interest Group on Computer Science Education), 2003, pp. 312–316.
  13. D. Kumar, D. Blank, T. Balch, K. O’Hara, M. Guzdial, and S. Tansley, “Engaging computing students with AI and robotics,” AAAI Spring Symp. - Tech. Rep., vol. SS-08-08, pp. 55–60, 2008.
  14. D. Blank, L. Meeden, and D. Kumar, “Python Robotics: An environment for exploring robotics beyond LEGOs,” in SIGCSE Bulletin (Association for Computing Machinery, Special Interest Group on Computer Science Education), 2003, pp. 317–321.
  15. D. Blank, D. Kumar, L. Meeden, and H. Yanco, “The Pyro toolkit for AI and robotics,” AI Mag., vol. 27, no. 1, pp. 39–50, 2006.
  16. D. Blank, D. Kumar, L. Meeden, and H. Yanco, “Pyro: A Python-based Versatile Programming Environment for Teaching Robotics,” ACM J. Educ. Resour. Comput., vol. 4, no. 3, 2004, doi: 10.1145/1083310.1047569.
  17. T. Balch et al., “Designing personal robots for education: Hardware, software, and curriculum,” IEEE Pervasive Comput., vol. 7, no. 2, pp. 5–9, 2008, doi: 10.1109/MPRV.2008.29.
  18. D. Kumar and L. Meeden, “Robot laboratory for teaching Artificial Intelligence,” in Poceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE, 1998, no. January, pp. 341–344, doi: 10.1145/273133.274326.
  19. P. Lollini, M. Mori, A. Babu, and S. Bouchenak, AMADEOS sysML profile for SoS conceptual modeling, vol. 10099 LNCS. Springer, 2016.
  20. E. A. Lee, “Cyber Physical Systems: Design Challenges,” 2008.
  21. G. Seif, “Your Guide to AI for Self-Driving Cars in 2020,” Towar. Data Sci., pp. 1–12, 2020, Online.. Available: https://towardsdatascience.com/your-guide-to-ai-for-self-driving-cars-in-2020-218289719619.
  22. SAE, “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles J3016_201806,” 2018.
  23. E. Guizzo, “How Google’s Self-Driving Car Works,” 2011.
  24. N. S. Yeshodara, N. S. Nagojappa, and P. Kishore N., “Cloud based self driving cars,” 2014 IEEE Int. Conf. Cloud Comput. Emerg. Mark. CCEM 2014.
  25. Y. Kang, “A Design on Teaching and Learning Method for Creative Talent in the FOURTH INDUSTRIAL REVOLUTION,” J-Institute, vol. 5, no. 1, pp. 1–9, 2020, doi: 10.22471/disaster.2020.5.1.01.

Article full text

Download PDF