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T-622-ARTI, Introduction to Artificial Intelligence, Spring 2014


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 good web site full of useful AI resources.

Coursework Overview

Assignments/Labs (25%)

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.

Final Project (20%)

You can choose a topic for the final programming project (discuss topics and find a group on the Piazza page. 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 in the last week (15% of the final grade).

Exam (35%)

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


Part of CourseTotal Weight
Assignments, Labs 25%
2 Programming Assignments (2*10%) 20%
Final Project 20%
Final Written Exam 35%
Total 100%

Course Schedule (subject to change)

1Jan 14 1,2Introduction, History, Agents
Jan 15 LabLab 1 - Agents
Jan 16 2Intelligent Agents
2Jan 21 3Search Problems, Blind Search
Jan 22 LabProgramming Assignment 1 - Search
Jan 23 3Blind Search, Heuristic Search
3Jan 28 3Heuristic Search
Jan 29 LabLab 2 - Hashing States
Jan 30 5Adversarial Search (Minimax, Alpha-Beta)
4Feb 04 5,6Adversarial Search (Algorithms), CSPs
Feb 05 LabProgramming Assignment 2 - Connect 4
Feb 06 6CSPs
5Feb 11 7Propositional Logic
Feb 12 LabLab 3 - CSPs
Feb 13 7Propositional Logic, Logical Agents
6Feb 18 7,8,9Logical Agents, First Order Logic
Feb 19 LabLab 4 - Propositional Logic
Feb 20 8,9,10First Order Logic, Planning
7Feb 25 10Planning
Feb 26 LabProgramming Assignment 2 - Competition
Feb 27 13, 14Uncertainty, Bayesian Networks
8Mar 04 13, 14Bayesian Networks
Mar 05 LabLab 5 - Bayesian Networks
Mar 06 18-21Machine Learning
9Mar 11 18.3Learning Decision Trees
Mar 12 LabLab 6 - Learning Decision Trees
Mar 13 25Robotics
10Mar 18 15Probabilistic Reasoning over Time
Mar 19 LabLab 7 - Particle Filtering
Mar 20 15Probabilistic Reasoning over Time
11Mar 25 ???
Mar 26 Lab???
Mar 27 Wrap-Up
13Apr 01 Project Presentations
Apr 02 LabProject Presentations
Apr 03 Project Presentations
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