Approaches to AI; higherorder logic; planning; expert systems; environment of AI systems; soft computing in AI systems; nonsymbolic learning; natural language processing; intelligent agent; multiagent system; semantic web; robotics.

Course Objectives

1. Analysing cognitive models

2.Analysing interaction models

3.Develop sample smart systems

Recommended or Required Reading

Luger, George; Stubblefield, William; 2004; “Artificial Intelligence: Structures and Strategies for Complex Problem Solving”; The Benjamin/Cummings Publishing; ISBN 0805347801 ,Russell, Stuart; Norvig, Peter; 2003; “Artificial Intelligence – A Modern Approach”; Prentice-Hall; 0131038052 ,Giarratano, Joseph C.; Ritel, Gary D.; 2005; “Expert Systems – Principles and Programming”; Thompson Course Technology; 0534384471 ,Munakata, Toshinori; 2007; “Fundamentals of the New Artificial Intelligence Neural, Evolutionary, Fuzzy and More”; Springer; 9781846288395 ,Maedche, Alexander; 2002 ; “Ontology learning for the semantic web”; Kluwer Academic Publishing; 0792376560 ,Hjelm, Johan; 2001; “Creating the semantic Web with RDF: professional developer s guide”; John Wiley; ,Goertzel, Ben; Pennachin, Cassio; 2007; “Artificial General Intelligence”; Springer; 9783540237334 ,Nabiyev, Vasif V.; 2005; “Yapay Zeka”; Seçkin Yayıncılık; 9753479859

Learning Outcomes

1. Develop smart behaviours

2. Programme in declarative or rule-based languages

3. Develops interactive systems

4. Develops distributed systems

Grading

Midterm 30%

Homework 10%

Research Presentation 30%

Final 30%