Artificial Inteligence

Course Code: Ν2-6020
Weekly Duty: 4 (2Th + 2L)
ECTS: 5
Typical Semester: 6th
Course Category: Specialty Course
Prerequisites:  

Learning Outcomes

The purpose of this course is to familiarize students with the theoretical space of artificial intelligence and its applications.

The aim of the course is that students acquire knowledge that will lead to practical application in areas such as knowledge representation, problem solving algorithms and inference techniques in rule-based systems and acquire academic knowledge on issues of concern today in the field of artificial intelligence, such as production trees, neural networks, genetic algorithms, intelligent agents and their applications.

More specifically, the learning objectives of the course are students, after completion of the course, be able to:

  • describe problems and represent relevant knowledge with formal methods
  • distinguish between blind and heuristic search algorithms and their coding in the context of solving problems
  • understand the different ways of knowledge representation
  • understand the structure and operation of expert systems
  • design and develop expert systems based on rules
  • recognize the different kinds of machine learning
  • describe the operation of machine learning systems, such as decision trees, neural networks and genetic algorithms
  • recognize the characteristics of intelligent agents and their applications.

Course Content

Introduction. Problem solving, Blind search algorithms, Heuristic search algorithms. Knowledge representation. Rule Based Systems. Machine learning (Decision trees, Neural networks, Genetic algorithms). Expert Systems engineering. Intelligent Agents. Applications of AI to Natural Language Processing, Vision, Planning and Robotics.

Practical work in the use of common lisp for developing search algorithms and clips for creating expert systems.

Literature
  1. Βλαχάβας, Ι., Κεφαλάς, Π., Βασιλειάδης, Ν., Ρεφανίδης, Ι., Κοκκοράς, Φ. & Σακελλαρίου, Η., Τεχνητή Νοημοσύνη, 3η έκδοση, Β. Γκιούρδας Εκδοτική, 2006.
  2. Russell, Stuart J., Norvig, Peter, Τεχνητή Νοημοσύνη : μια σύγχρονη προσέγγιση, Αθήνα : Κλειδάριθμος, 2007.
  3. Γεωργούλη, Κ., Εισαγωγή στην Τεχνητή Νοημοσύνη, Σημειώσεις του Μαθήματος, Αιγάλεω, 2004.
  4. T. Dean, J. Allen, Y. Allimonos, Artificial Intelligence, Theory and Practice,Benjamin/Cummings, 1995.
  5. J. Finlay, & A. Dix, An Introduction to Artificial Intelligence, UCL Press, 1996.
  6. P. Winston, Artificial Intelligence, Addison-Wesley, 1992.
  7. S. Russel and P. Norvig, “Artificial Intelligence: A Modern Approach”, 4rth ed., Prentice Hall, 2006.
  8. E. Rich and K. Knight, “Artificial Intelligence”, 2nd ed., McGraw-Hill, 1992.
  9. P.H. Winston, “Artificial Intelligence”, 3rd ed., Addison-Wesley, 1992.
  10. N. Nilsson, “Artificial Intelligence: A New Synthesis”, Morgan Kaufmann, 1998.
  11. R.J. Schalkoff, “Artificial Intelligence: An Engineering Approach”, McGraw-Hill, 1990.
  12. E. Charniak and D.M. Dermott, “Introduction to Artificial Intelligence”, Addison-Wesley, 1985.
  13. T.J. Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, 1995.
  14. S. Haykin, “Neural Networks: A Comprehensive Foundation”, MacMillan, 1994.
  15. D. Goldberg, “Genetic Algorithms in Search,Optimization and Machine Learning”, Addison-Wesley, 1989.
  16. “Building Expert Systems”, F. Hayes-Roth, D.A. Waterman and D.B. Lenat (Eds.), Addison-Wesley, 1983.
  17. Huhns, Michael N., Singh, Munindar P., Readings in Αgents, San Francisco, Calif : Morgan Kaufmann , c1998 .
  18. Bigus, Joseph P., Bigus, Jennifer Constructing intelligent agents with Java :a programmer’s guide to smarter applications, New York : Wiley, 1998.
  19. Bellifemine, Fabio Luigi, Caire, Giovanni, Greenwood, Dominic, Developing multi-agent systems with jade, Hoboken, NJ : John Wiley, 2007.

Internationalisation I18n