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T-622-ARTI, Introduction to Artificial Intelligence, Spring 2013

Basic Info


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.


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.


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/Labs (15%)

You hand in the (almost) weekly assignments and finish the labs. The assignments 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. The labs are more practical applications of the material, often in the form of small programming tasks.

Programming assignments (2 x 10%)

You complete two programming assignments. This can be done as a group project (up to 4 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.

The second programming assignment is to program a Connect-4 agent.

T-622-ARTI. Spring 2013 - Final Project (25%)

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 4 people). You have to hand in a 1-2 page description of the project goal and some ideas on how to achieve it approx. in week 8 (5% of the final grade) and a report and demonstration in week 12 (20% of the final grade).

List of chosen topics

Exam (40%)

There will be a final exam (3h) with questions similar to the ones in the assignments.

Course Schedule (subject to change)

1Jan 14 1,2Introduction, History, Agents
Jan 17 LabLab 1 - Agents
Jan 18 2Intelligent Agents
2Jan 21 3Search Problems, Blind Search
Jan 24 LabProgramming Assignment 1
Jan 25 3Blind Search, Heuristic Search
3Jan 28 3Heuristic Search
Jan 31 LabLab 2 - Formulating Search Problems
Feb 01 3Heuristic Search
4Feb 04 5Adversarial Search (Minimax, Alpha-Beta)
Feb 07 LabLab 3 - Hashing States
Feb 08 5Adversarial Search (Algorithms)
5Feb 11 Monte-Carlo Search, General Game Playing
Feb 14 LabProgramming Assignment 2
Feb 15 7Propositional Logic
6Feb 18 7Propositional Logic, Logical Agents
Feb 21 LabLab 4 - Propositional Logic
Feb 22 7Logical Agents
7Feb 25 7,8,9Logical Agents, FOL
Feb 28 Labtime limits and caching with Alpha-Beta Search
Mar 01 8,9FOL
8Mar 04 10Planning
Mar 07 LabProgramming Assignment 2 - Competition
Mar 08 10Planning
9Mar 11 13, 14Uncertainty, Bayesian Networks
Mar 14 LabLab 5 - Bayesian Networks
Mar 15 14Bayesian Networks
10Mar 18 18-21, 18.3Machine Learning, Learning Decision Trees
Mar 21 LabLab 6 - Learning Decision Trees
Mar 22 25Robotics
11Mar 25 15Probabilistic Reasoning over Time
12Apr 04 LabLab 7 - Particle Filtering
Apr 05 Wrap-Up
13Apr 08 Project Presentations
Apr 11 LabProject Presentations
Apr 12 Project Presentations
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