Advanced Artificial Intelligence Topics

Professors George Vouros
Course category CM/CIS
Course ID DS-411
Credits 5
Lecture hours 3 hours
Lab hours 2 hours
Digital resources View on Evdoxos (Open e-Class)

Learning Outcomes

Upon successful completion of this course, students should be able to know and develop basic decision making abilities of intelligent agents who are capable of acting in the real world.

Specifically, students acquire knowledge and the abilities to develop and apply

  • planning algorithms
  • methods for re-planning and computing actions’ schedules for acing in the real world
  • knowledge representation and reasoning with ontologies and real-world data
  • basic principles and algorithms for (simple or advanced) decision making
  • algorithms for learning policies towards acting in the real world

Through a critical view of methods and by acquiring experience in building systems in paradigmatic cases.

Course Contents

  • Basic and advanced planning algorithms
  • Replanning and scheduling actions with duration.
  • Reasoning and representation with ontologies and data
  • Decision making principles and methods
  • Reinforcement learning, introduction.

Recommended Readings

  • Stuart Russel and Peter Norvig. Artificial Intelligenc­e: A Μodern Approach, Prentice Hall, 2nd edition (2003). http://aima.cs.berkeley.edu/.
  • Yoav Shoham, Kevin Leyton-Brown Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations, Cambridge University Press, 2009

Associated scientific Journals

  • Artificial Intelligence, Elsevier, ISSN: 0004-3702
  • Journal of Web Semantics, Elsevier, ISSN: 1570-8268