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T-622-ARTI, Introduction to Artificial Intelligence, Spring 2012

Basic Info

Description

Artificial Intelligence (AI) is devoted to the computational study of intelligent behaviour, including areas such as problem solving, knowledge representation, reasoning, planning and scheduling, machine learning, perception and communication. This course gives an overview of the aforementioned AI subfields from a computer science perspective and introduces fundamental solution techniques for addressing them. An important part of the course is an independent final project where the students develop AI software in an area of their choice.

Goals

On the completion of the course the students should:

  • have a good overview of the field of artificial intelligence (AI) and a thorough understanding of the fundamental solution methods used to attack a wide variety of AI-related problems.
  • have gained experience building a small special-purpose AI system.

Book

The textbook for this class is: “Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig. This book has a very good web site full of useful AI resources.

Coursework Overview

Assignments (10%)

You hand in the (almost) weekly assignments. These will mainly consist of small exercises in which you have to apply what you should have learned in the lecture. The questions should give you an indication of the questions that may be asked in the final exam.

Programming assignments (2 x 10%)

You complete two programming assignments. This can be done as a group project (up to 3 people). Make sure you clearly indicate who is part of the group and that every group member clearly understands the solution.

The first programming assignment is to use search to find a good solution for a vacuum cleaning robot.

Final Project (30%)

You can choose a topic for the final programming project (discuss topics and find a group in the forum). Like the programming assignments, this can be done as a group project (up to 3 people). You have to hand in a 1-2 page description of the project goal and some ideas on how to achieve it in week 7 (10% of the final grade) and demonstrate the final project in week 12 (20% of the final grade).

Course Schedule

WeekDateChaptersTopic
1Jan 10 1Introduction+History
Jan 12 2Intelligent Agents
2Jan 17 3Search Problems
Jan 19 3Blind Search
Jan 19 LabLab 1 - Agents
3Jan 24 3Heuristic Search
Jan 26 3Heuristic Search
Jan 26 LabLab 1 - Agents
4Jan 31 3Heuristic Search
Feb 02 5Adversarial Search
Feb 02 LabLab 2 - Formulating Search Problems
5Feb 07 Guest Lecture (Yngvi Björnsson)
Feb 09 Guest Lecture (Hannes Högni Viljámsson)
6Feb 14 5Adversarial Search
Feb 16 7Prop-Logic
Feb 16 LabReview of Programming Assignment 1, Programming Assignment 2

Grading

Part of CourseTotal Weight
Assignments (10*1%) 10%
2 Programming Assignments (2*10%) 20%
Final Project 30%
Final Written Exam 40%
Total 100%
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