Overview

Algorithms and representations used in artificial intelligence. Introduction and implementation of algorithms for search, planning, perception, knowledge representation, logic, probabilistic representation and reasoning, robotics, and machine learning.

Some portions of the course will be based on material from Ron Parr's Spring 2014 Intro to AI course.

Instructor
George Konidaris
Office: North 133
Office Hours: Mondays 5-6pm, Tuesdays 12-1pm
Email: gdk at cs dot duke dot edu

TA
Cameron Allen
Office: N141 North
Office Hours: Wednesdays 11:30am-12:30pm, Thursdays 11am-12pm, at LSRC D309
Email: cam at cs dot duke dot edu

UTAs
Diego Farias Canepa
Office Hours: Mondays 6-9pm at The Link (Perkins Library)
Email: diego dot farias dot canepa at duke dot edu

Kaighn Kevlin
Office Hours: Tuesdays 12:55-2:55pm at The Link (Perkins Library)
Email: kaighn dot kevlin at duke dot edu

Yixin Lin
Office Hours: Thursdays 4:30pm-7:30pm at The Link (Perkins Library)
Email: yixin dot lin at duke dot edu

Ying Qi
Office Hours: Tuesdays 7-9pm, Fridays 12-1pm, at The Link (Perkins Library)
Email: ying dot qi at duke dot edu

Yilun Zhou
Office Hours: Mondays 9am-12pm, at North 206.
Email: yilun at cs dot duke dot edu

[Back to top]

Syllabus

1. Agents and Agenthood
2. Search
   • Uninformed
   • Informed
   • Mini-Max for Game Playing
3. Knowledge Representation and Reasoning
   • Propositional Logic
   • First-Order Logic
   • Reasoning and Logical Inference
   • Uncertain Knowledge
     • Bayes' Rule
     • Probabilistic Reasoning
     • Bayes Nets
4. Planning
   • Task Planning
   • Robot Motion Planning
5. Learning
   • Supervised Learning
   • Unsupervised Learning
   • Reinforcement Learning
6. Philosophy of AI

[Back to top]

Prerequisites

You should be comfortable independently writing and debugging programs in C, C++, or Java. (The actual assignments with be in Python, but there will be a Python tutorial early in the semester.)

You should be able to do short proofs.

You should be comfortable with the core computer science concepts of computational complexity and the analysis of algorithms.

You should know some calculus. Basic probability and statistics are helpful but not required.

[Back to top]

Schedule

The first class is on Wednesday January 13th. The class meets on a Monday-Wednesday schedule, from 3:05pm to 4:20pm in LSRC B101.

Note that the schedule below is tentative, and may be revised as we go along.

[Back to top]

Assignments

What to turn in:

• For written answers, please turn in a PDF. Do not turn in Microsoft Word or other formats.


• For programming questions, please include your code and any supporting files needed, along with some examples of how your code runs. If any special instructions are needed for compiling or executing your code, please include this in a readme.txt file. If it is not obvious how to make your code work, you may not receive full credit!


• It is not necessary to embed an extra copy of your code within your written solutions. The files you submit are sufficient for this purpose. However, if you want to reference something in one your files, please do so in a clear manner by mentioning specific file names and functions.


• Include your name in all files that you submit. Put it at the top of the first page of your written solutions and in the comments to each file you submit for your code.


• Please avoid turning in scanned, handwritten assignments. This is permitted but discouraged because slows down the grading process and because it leads to very large files. If there is some emergency that necessitates submitting scanned material, pick a DPI and compression level that will lead to reasonable file sizes.


• Include figures directly in your PDF rather than as separate attachments.

How to turn it in:

• Unless otherwise stated, solutions are due immediately before class on the due date.


• Homework assignments should be turned in electronically through Sakai.


• Include your name as part of the file name of the file you upload to sakai. (It is confusing for the TAs if they need to wade through 60 files all named hw.pdf.) If your assignment involves multiple files, please put them in a single folder/directory and zip them. Do not use other compression methods such as zoo, rar, etc.

Academic Honesty

• We take academic honesty very seriously. This matrix should leave no ambiguity about what is permitted and what is not permitted. You should check if you have any confusion about what is permitted.

Lateness policy

• You may request an extension before the due date of the assignment. Valid reasons for extensions include (but are not necessarily limited to) interviews, travel for research or academic purposes, and illness.


• Late assignments (without a previously granted extension) will be penalized 10% per day. Assignments will not be accepted more than 5 days after the due date.

[Back to top]

Grading

Graded components will be the homeworks (40%), the midterm (30%), and the final exam (30%).

I expect all Duke students to conduct themselves with the highest integrity, according to the Duke Community Standard. If you are unsure what this means, please refer to this link. For a more concrete description, this matrix outlines what forms of collaboration with others are and are not allowed during this course.

[Back to top]

Resources

Required Text

Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig. Be sure to check for errata! (scroll down for a list of current errata)

[Back to top]