Aim
To give an introduction to several subdomains of artificial intelligence and to give an orientation about fundamental methods within these domains. To convey knowledge about breath and depth of the domain.

Knowledge and understanding
For a passing grade the student must

• display basic knowledge concerning theories and methods related to the following subdomains: intelligent agents, heuristic search, game programming, knowledge representation, knowledge-based systems, genetic algorithms, machine learning, natural language processing.

Skills and abilities
For a passing grade the student must

• complete a number of assignments based on problems related to the following subdomains: intelligent agents, game programming, knowledge-based systems, machine learning, natural language processing.

Contents
Heuristic search. Game programming. Knowledge based systems. Neural nets. Genetic algorithms. Machine learning based on classification. Intelligent agents. Natural language. Semantic Web.

Literature
Russell, S & Norvig, P: Artificial Intelligence - A Modern Approach, 2nd Ed. Prentice Hall 2003. ISBN: 0-13-080302-2

Code: 0104. Name: Written Examination.
Higher education credits: 3. Grading scale: TH. Assessment: Written examination. The final grade of the course is based on the result of this examination and on the compulsory assignments. Contents: Written examination.

Code: 0204. Name: Assignments.
Higher education credits: 4,5. Grading scale: UG. Assessment: To pass the course all the compulsory assignments must be approved. Details regarding the compulsory assignments will be found in the course program (syllabus) at the course web site. The final grade of the course is based on the result of the written examination and the quality of the compulsory assignments. Contents: Assignments related to some of the treated subdomains are implemented to give practical experience of difficulties such as computational complexity, scalability and result interpretation.