have a basic understanding of the concept of intelligent agents
have overview of searching as a problem solving approach
have an understanding of the concept of knowledge bases and expert systems
Course contents
History of AI
Intelligent agents
Solving problems by searching, heuristic search strategies
Local search and optimization problems
Adversarial search, games, alpha-beta pruning
Constraint satisfaction problems
Introduction to expert systems
Teaching methods
The course is organized with a combination of lectures, assignments, paper studies, labs, and report writing. The tasks are done individually or in small groups with group supervision. The workload for the average student is approximately 135 hours.
Assessment methods and criteria
3 hours written exam (50%). Portfolio assessment (50%). Information about the content of the portfolio will be given in Canvas at the start of the semester for each seminar. Graded assessment.
Evaluation
The person responsible for the course decides, in cooperation with student representative, the form of student evaluation and whether the course is to have a midway or end of course evaluation in accordance with the quality system for education, chapter 4.1.