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 Aristarchus (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