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Introduction

This course provides an introduction to the field of Artificial Intelligence (AI). Computers are capable of performing an amazing range of complex tasks. However, seemingly simple tasks, such as understanding spoken discourse, collecting information about the cheapest air ticket across the Web, or walking down a hallway, can be tremendously difficult for a computer system. AI embraces these difficult tasks as it expands the capabilities of modern computer systems.

This course focuses on techniques and approaches that are successfully used in making computers more 'intelligent'. Areas of study include: agents, search, knowledge representation, planning, and learning. Other areas are briefly covered for a more complete picture of the AI spectrum.

Course Coordinators

• Until May 15: Adi Botea - email: adi.botea-AT-nicta.com.au - building: NICTA
• After May 15: Patrik Haslum - email: patrik.haslum-AT-nicta.com.au - building: NICTA

Lecturers

• Adi Botea - email: adi.botea-AT-nicta.com.au - building: NICTA
• Tiberio Caetano - email: tiberio.caetano-AT-gmail.com - building: NICTA
• Patrik Haslum - email: patrik.haslum-AT-nicta.com.au - building: NICTA
• Marcus Hutter - email: marcus.hutter-AT-anu.edu.au - building: RSISE
• Philip Kilby - email: philip.kilby-AT-anu.edu.au - building: NICTA
• Jochen Renz - email: jochen.renz-AT-anu.edu.au - building: RSISE

Tutors

• Debdeep Banerjee - email: Debdeep.Banerjee-AT-rsise.anu.edu.au - building: NICTA
• Dmitry Kamenetsky - email: dmitry.kamenetsky-AT-rsise.anu.edu.au - building: NICTA
• Jason Li - email: jason.li-AT-rsise.anu.edu.au - building: NICTA

Unit Prerequisites

COMP2100 or COMP2500; and COMP2600

Objectives

This course introduces students to the exciting field of Artificial Intelligence. The course provides an introduction to the theoretical foundations and practical approaches used to solve complex problems. The subject will provide a foundation for students embarking on honors or postgraduate research in this area. (The College of Engineering and Computer Science currently has many PhD/Honors students conducting research in AI.) This course also aims to give students, who will go on to work within the IT industry, a background in AI so they know when and how best it may be applied.

The course will enable students to grasp AI in its many different aspects and approaches. Given an AI problem, students who successfully complete the course will understand how it fits within the field and know what are the most suitable approaches to solving the problem. This course will improve students ability to think through complex issues and problems. It will also improve students programming abilities as the labs will involve completing a variety of programming tasks.

Textbook

Textbook : Artificial Intelligence - A Modern Approach, S. Russell and P. Norvig, Prentice Hall, 2nd Edition, 2003.

It is strongly recommended that students obtain a copy of the textbook.

Assessment

Assignments (50%), and final exam (50%).
The assignments are an extension of the four laboratories.
Note that consistent scaling across all students may occur with these marks.
The minimum final mark required for a pass grade is 50%.
Both the assignments and the final exam should have a significant contribution to the final mark. More precisely, to obtain a passing grade, students should get at least 40% of the assignment marks and at least 40% of the final exam marks.
Supplementary exams will be awarded only to those students who meet the following criteria:

The student has obtained a final mark between 45 and 49, and meets the requirement of having obtained at least 40% of exam and assignment marks. By getting a bare pass (50) in the supplementary exam, they will pass the course. (This will require a class mark of 50 or over.)

Check the Undergraduate Handbook and the Web for policy statements concerning special consideration and special, further, and supplementary examinations.