Artificial Intelligence
Professors George Vouros
Course category Core
Course ID DS-518
Credits 5
Lecture hours 3 hours
Lab hours 2 hours
Digital resources View on Aristarchus (Open e-Class)

Learning Outcomes

Upon successful completion of this course, students should be able:

  • to explain fundamental concepts including agents, problem & state/action/spaces, problem solving via search as a model of thinking, heuristics, knowledge representation and reasoning using logic.
  • to select algorithms for problem solving based on problems characteristics, and characteristics of problem & state/action spaces.
  • to evaluate usefulness, advantages and limitations of alternative algorithms and methods towards increasing computational effectiveness of problem solving
  • to modelling problems as constraint problems, or as problems for proving in logic.

Towards the construction of advanced methods of problem solving.

Course Contents

  • Introduction to artificial intelligence, goals, advances, prospects, limitations, and basic notions regarding agents and problem solving
  • Blind search algorithms
  • Informed search algorithms and proofs of finding optimal solutions
  • Heuristic functions and their construction and selection
  • Local search using hill climbing, simulated annealing, local beam search, genetic algorithms
  • Constraint problem solving: From basic to advanced arc consistency techniques
  • Knowledge representation and reasoning in logic
  • Advanced reasoning algorithms

Recommended Readings

  • Stuart Russel and Peter Norvig. Artificial Intelligenc­e: A Μodern Approach, Prentice Hall, 2nd edition (2003). http://aima.cs.berkeley.edu/. Το βιβλίο έχει εκδοθεί στα Ελληνικά από τις εκδόσεις Κλειδάριθμος με τον τίτλο «Τεχνητή Νοημοσύνη: Μια σύγχρονη προσέγγιση».http://aima.uom.gr/.
  • Ι. Βλαχάβα, Π. Κεφαλά, Ν. Βασιλειάδη, Φ. Κόκκορα και Η. Σακελαρίου. Τεχνητή Νοημοσύνη. Εκδοτικός οίκος «Β. Γκιούρδας Εκδοτική – Μονοπρόσωπη ΕΠΕ».http://aibook.csd.auth.gr.
  • Nilsson, N., Artificial Intelligence: A New Synthesis, San Francisco: Morgan Kaufmann, 1998. Nilsson, N., Principles of Artificial Intelligence, San Francisco: Morgan Kaufmann, 1980.David Poole, Alan Mackworth and Randy Goebel. Computational Intelligence: A Logical Approach, Oxford University Press, New York, 1998. http://www.cs.ubc.ca/spider/poole/ci.html.
  • Matthew L. Ginsberg. Essentials of Artificial Intelligence, Morgan Kaufmann, 1993.
  • Elaine Rich and Kevin Knight, Artificial Intelligence, 2nd edition, Mc Graw Hill, 1990.
  • Genesereth and N. Nilsson: Logical Foundations of Artificial Intelligence, Morgan Kaufmann, 1987
  • J. Brachman and H.J. Levesque, ”Knowledge Representation and Reasoning”, Morgan Kaufmann, 2004.

Associated scientific Journals

  • Artificial Intelligence, Elsevier, ISSN: 0004-3702
  • Expert Systems with Applications, Elsevier, ISSN: 0957-4174
  • IEEE Trans. On Pattern Analysis and Machine Intelligence, IEEE, ISSN 01628828