Overview

Artificial Intelligence is the area of computer science that concerns itself with "smart" solutions to problems. How can we find solutions faster than typical brute-force approaches by using knowledge about the problem? How can we use higher-orders of representation such as logic, graph structures, and others to solve problems? How can we learn from data or from the environment? How can we adapt the biological mechanisms of intelligence for computational purposes?

We'll consider all of the above as we examine a variety of problems in AI, including agent-based behavior (as in robotics), heuristic search, strategy game playing, supervised learning, reinforcement learning, logical deduction, and others.

Instructor Information

• Instructor: Dave Musicant
• Office: CMC 326
• Email: dmusican
• Office phone: (507)222-4369
• Office hours: See home page at http://www.cs.carleton.edu/faculty/dmusican

Grader:

• Charles Noneman (email: nonemanc)

Textbook

• Artificial Intelligence: A Modern Approach, Stuart Russell & Peter Norvig, 2nd edition, Prentice-Hall, 2002.

Important Dates

• Exam 1: Friday, April 18
• Exam 2: Friday, May 9
• Exam 3: Friday, May 30
• Final project due: Monday, June 9

Your Grade

• Assignments: 35%
• Exam 1: 20%
• Exam 2: 20%
• Exam 2: 20%
• Class project: 5%

Assignments & Class Project

• The assignments will include both non-computer activities and programming.
• The class project is due at the end of the term. You can choose to do essentially anything inside the area of AI, so long as I approve it in advance. You can build on material we do in class, or you can use it as an opportunity to learn about subject matter we don't have time to cover.

Collaboration

1. Non-computer assignments: You should turn in your own assignment. You may work with other people, but each of you should turn in your own.
2. Computer assignments: You may work together on these in pairs, if you wish, and I encourage you to do so. Include everyone's names in documentation at the top. Make sure to cite any ideas you get from other people.
3. Take-home exams: Do these completely on your own. You can discuss them only with me.
4. Final project: You may do this in pairs, if you wish.

Collaborating

• Collaborating is good.
• You are encouraged to collaborate on ideas, essays, homework problems, and program design.
• Learning is often a social effort, and there is much that you can gain by talking out the ideas in this class with each other.
• You can by all means talk to each other, look at each other's programs and ideas to help fix problems, and share thoughts.

Plagiarism

• Plagiarism is bad. DON'T DO IT!
• Any assignments that you turn in should be your own work. If you are turning in a computer program, it should ultimately be the work of the people on your team only (if you worked in a team).
• You may share thoughts and ideas in programs with others, but you must appropriately reference the source in program comments or with references at the end of your document.

• Taking someone else's program or assignment, making changes (such as changing variable names), and turning it in.
• Finding a similar program or assignment on the Internet, making changes, and turning it in.
• Finding a similar program or assignment in a book, making changes, and turning it in.

Working from Home

Most of the programming that we do will be in Python, which will be set up for use in the Computer Science labs. You should feel free to install Python on your home machine and work from there if you wish. If you do so, you are responsible for making sure that your programs work under the version of the compiler that we have installed in the labs. I am glad to informally provide whatever advice I can to help you get the software running on your own machine, but home use is technically "unsupported."

Homework Policy

• Each assignment will have a specific time for which it will be due. An assignment turned in late within one day of the due time will be docked 25%. A program turned in later than one day of the due date but within two days will be docked 50%. An assignment turned in any time after this until the last day of classes will be docked 75%.
• College policy dictates that there can be no grace period on the final project.

Details

• Chapter 1: Introduction
• Chapter 2: Intelligent Agents
• Chapter 3: Solving Problems by Searching
• Chapter 4: Informed Search and Exploration
• Chapter 6: Adversarial Search
• Chapter 18: Learning from Observations
• Chapter 20: Statistical Learning Methods
• Chapter 21: Reinforcement Learning
• Chapter 7: Logical Agents
• Chapter 8: First Order Logic
• Chapter 9: Inference in First-Order Logic
• Chapter 11: Planning
• Chapter 12: Planning and Acting in the Real World
• Chapter 13: Uncertainty
• Chapter 14: Probabilistic Reasoning Systems
• Chapter 15: Probabilistic Reasoning Over Time
• Chapter 16: Making Simple Decisions
• Chapter 25: Robotics
• Chapter 26: Philosophical Foundations