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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.

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

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 8 (10% of the final grade) and demonstrate the final project in week 12 (20% of the final grade).

Project topics:

  • Andrea Monacchi “An Early Warning System for Ambient Assisted Living” (part of a master thesis)
  • Axel Gauti Guðmundsson, Heiðar Þórðarson “Dr. Dragon: Starcraft bot extraordinaire”
  • Bæring Gunnar Steinþórsson, Viktor Þorgeirsson “Chat bot”
  • Baldur Már Helgason, Jökull Jóhannsson “Path following robot”
  • Guðmundur Siemsen Sigurðarson, Ingi Steinn Guðmundsson “Starcraft: Attack or Flee”
  • Oddur Aðalgeirsson, Rúnar Freyr Rúnarsson “Starcraft: Ranged vs. Melee Unit”
  • Ólafur Unason “Assassin Robot in Robocode”

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)
6Feb 14 5Monte-Carlo Search, General Game Playing
Feb 16 7Propositional Logic
Feb 16 LabReview of Programming Assignment 1, Programming Assignment 2
7Feb 21 8Propositional Logic
Feb 23 8,9FOL
Feb 23 LabLab 3 - Logic
8Feb 28 10Planning
Mar 01 LabConnect-4 Tournament
9Mar 06 13,14Uncertainty, Bayesian Networks
Mar 08 18-21Learning
Mar 08 LabLab 4 - Bayesian Networks
10Mar 13 18-21Learning
Mar 15 25Robotics
Mar 15 LabLab 5 - Learning Decision Trees
11Mar 20 25Robotics
Mar 22 Probabilistic Reasoning over Time
Mar 22 LabLab 6 - Particle Filtering
12Mar 27 Wrap-Up
Mar 29 Project Presentations
Mar 29 LabProject Presentations

Pathfinding Search (lab material) | | |

Feb 04 5Adversarial Search
5Feb 07 5Guest: Deon Garrett Q
Feb 10 LabFormulating Search Problems (lab material)PROB1PROG1
Feb 11 5Adversarial Search (continued)
6Feb 14 7Guest: Yngvi Björnsson Q
Feb 17 LabReview of PROG1 PROG2PROB1
Feb 18 7Propositional Logic
7Feb 21 8Common Sense Discussion Q
Feb 24 LabReview of PROB1 FP-PROP
Feb 25 8First-Order Logic
8Feb 28 Guest: Claudio Pedica Q
Mar 03 LabPowerLoom (lab material) PROG2
Mar 04 10PlanningPROB2
9Mar 07 10Guest: Ari Jónsson Q
Mar 10 Lab Connect 4 Tournament
Mar 11 13,14Uncertainty / Bayesian nets
10Mar 14 NAGuest: Kristinn R. Thórisson Q
Mar 17 LabBayesian Nets (lab material) PROB2
Mar 18 18-21Learning
11Mar 21 NA“Guest:” Hannes Q
Mar 24 LabBayesian Nets Excercise + Review of PROB2
Mar 25 Exam Review
12Mar 28 Other AI Resources and Discussions
Mar 31 Final Project Demos / Presentations FP
Apr 01 Final Project Demos / Presentations FP

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Grading

Part of CourseTotal Weight
Assignments (10*1%) 10%
2 Programming Assignments (2*10%) 20%
Final Project 30%
Final Written Exam 40%
Total 100%
/var/www/ailab/WWW/wiki/data/pages/public/t-622-arti-12-1/main.txt · Last modified: 2012/03/22 11:24 by stephan