Artificial Intelligence COMP3620/COMP6320
Welcome to the AI course at the ANU !The course covers a range of different topics within the field of AI, each taught by a specialist in that area. The Introduction and Search is taught by Sylvie Thiebaux who is also the convenor and general contact person of the course, Knowledge Representation and Reasoning by Miquel Ramirez, Planning by Sylvie again and Reinforcement Learning by Marcus Hutter. The main website for communication during the course is the wattle page.
Formalities/Miscellaneous/SummaryOffered By: The AI Group @ Research School of Computer Science @ Australian National University
Offered In: First Semester, 2015
Lecturers: Prof. Sylvie Thiebaux (Convenor), Dr. Miquel Ramirez, Prof. Marcus Hutter
Tutors/Labs: Dr. Miquel Ramirez, Dr. Enrico Scala
Target: Undergraduate (COMP3620) and Graduate (COMP6320) students. Others welcome.
Enrollment: Undergraduates &Masters: The usual way via ISIS. Honors&Others: Contact lecturer.
Admin: Ms. Bindi Mamouney.
Unit Value: 6 units
Time Table: See http://timetable.anu.edu.au.
Assessment: Assignments (50%); Exam (50%). Students must achieve at least 50% overall and 40% on the final exam to pass.
Textbook: Course textbook is "Artificial Intelligence - A Modern Approach"(3rd edition), by Stuart Russell and Peter Norvig (Prentice Hall). This book gives a comprehensive tour of AI, and not all of it is part of the course material. Details about which chapters are included and additional recommended reading will be given by the lecturers for each topic.
Programs and Courses page: http://programsandcourses.anu.edu.au/course/COMP3620
Wattle page: http://wattlecourses.anu.edu.au/course/view.php?id=127322
This page: http://cs.anu.edu.au/courses/COMP3620/
Prerequisites: What you really need is some background in computer science and programming, as well as some knowledge of logic. As stated in programs and courses, this has been translated into the following formal requirements:
- computer science/programming: COMP1100 or COMP1130 and COMP1110 or COMP1140 or COMP1510
- logic: COMP2620 or COMP2600
Learning OutcomesAfter completing this course, students should be able to:
- Identify problems that are amenable to solution by AI methods, and which AI methods may be suited to solving a given problem.
- Formalise a given problem in the language/framework of different AI methods (e.g., as a search problem, as a constraint satisfaction problem, as a planning problem, as a Markov decision process, etc).
- Implement basic AI algorithms (e.g., standard search algorithms or dynamic programming).
- Design and carry out an empirical evaluation of different algorithms on a problem formalisation, and state the conclusions that the evaluation supports.