MACS 404 Fall 2006

Artificial Intelligence

 

Instructor: Dr. Roman Tankelevich

Office / office phone: 209 Stratton Hall, 303-273-3581

Office Hours: MWF 9:00-10:00 am

Also, by appointment!

Email: rtankele@mines.edu

Home Page URL:  www.mines.edu/~rtankele

Class schedule: MWF 12:00-12:50 p.m.       

Assessment:

·         Assignments and Projects (25%)

·         One midterm exam (25%)

·         Presentations, seminar participation (25%)

·         One comprehensive final exam (25%).

 

General Course Description

 

This course is designed to develop a general understanding of one of the newest sciences, Artificial Intelligence. It comprises many disciplines and various problems some of which have not been solved yet which makes this study both fascinating and challenging. In this class, we will work towards understanding of what “intelligent behavior” means, what intelligence is, and what makes a system intelligent.

We will follow the textbook referenced below but not always very closely. In the spirit of learning by doing, we will emphasize the practical study of the AI technique. A few home works and projects will be posted. Some of them are in form of team projects by groups of two to three students with each student working on a specific part of the project. Each team will have a time slot to present the results in class. The quality of the written report and presentation of the project will be graded.

Since creating AI ideas and using its technique requires skills, effort, and critical thinking students will be asked to actively discuss the topics under study in class. Students will also be invited to prepare some topics from the book to present them in front of class. These activities will be generously awarded!

Different fields can benefit from the AI and robotics is one of them. Robotics bridges the fields of artificial intelligence, engineering, and cognitive science. It is only natural for us here, at an engineering school, to emphasize importance of interlinks between AI and robotics. All the aspects of the AI that can relate to robotics will be studied in its context. Our approach is based on the theory of intelligent agents complemented with the principle of embodiment – agents have a body that can interact with their environment (some scholars even argue that it would be meaningless to study mind without body).  We will develop algorithms and software modules that can be used in a robotic prototype: agent’s architecture, search (informed, uninformed, with constraints), planning, logics and first-order inference, uncertain knowledge and reasoning, communications and formal grammar, computer vision, motion, and other aspects of intelligent robotics.

Our study of AI requires the programming work and, sometimes, the use of unusual languages. We are not studying those tools here as such! Instead, we will just look at their specifics to understand why they are important. You may use these languages while working on the projects. Also, such traditional and much more familiar languages as C++ or Java can be used also in your work on the projects.

Textbook (Required!)
Stuart Russell and Peter Norvig, Artificial Intelligence A Modern Approach, 2nd edition, 2003 (you can also use the 1st edition, 1995 of the book).

Course basic topics and tentative syllabus

 

 

1.                   Artificial Intelligence – principles, history, most important tasks. State of the art.

Basics of robotics.

Chapter 1 Assignment: Search Internet for AI hottest topics (swarms, multi-agents, robotics, etc.)

Aug 21 – 25

2.                   Intelligent Agents. Rational agents. Learning and autonomy. Agents in simulation of dynamic systems. 

Chapter 2

Aug 28 – Sep 1

3.                   Solving problems by searching

Chapter 3

Sep 4 – 8

4.                   Informed search

Chapter 4

Sep 11 – 15

5.                   Constraint Satisfaction Problems

Chapter 5

Sep 18 – 22

6.                   Adversarial Search

Chapter 6

Sep 25 – 29

7.                   Logical Agents

Chapter 7

Oct 2 – 6

8.                   First-order logic

Chapter 8

Oct 9 – 13

9.                   Inference in First-order logic. Midterm exam.

Chapter 9

Oct 16 – 20

10.                Planning. Multiagents.

Chapter 11-12

Oct 23 – 27

11.                Uncertain knowledge and reasoning. Fuzzy sets and control.

Chapter 13-14

Oct 30 – Nov 3

12.                Neural Networks. Learning

Chapter 19, 20

Nov 13 – 17

13.                Communication, formal grammars

Chapter 22

Nov 20 – 24

14.                Perception in robotics.

Chapter 24-25

Nov 27 – Dec 1

15.                Philosophical Foundations. AI: Present and Future.

Chapter 26-27

Dec 4 – Dec 8