OMS CS7637: Knowledge-Based AI – Fall 2018

This page provides information about the Georgia Tech OMS CS7637 class on Knowledge-Based AI relevant only to the Fall 2018 semester. Note that this page is subject to change at any time. The Fall 2018 semester of the OMS CS7637 class will begin on August 20, 2018. Below, find the course’s calendar, grading criteria, and other information. For more complete information about the course’s requirements and learning objectives, please see the general CS7637 page.

Quick Links

To help with navigation, here are some of the links you’ll be using frequently in this course:

Course Calendar At-A-Glance

Below is the calendar for the Fall 2018 OMS CS6750 class. Note that assignment due dates are all Sundays at 11:59PM Anywhere on Earth time.

Week #Week OfLessonsDeliverableAssignment Due Date
108/20/201801, 02Introductions, Start-of-Course Survey08/26/2018
208/27/201803, 0409/02/2018
309/03/201805, 06Homework 109/09/2018
409/10/201807, 08Peer Feedback09/16/2018
509/17/201809Project 1, Quarter-Course Survey09/23/2018
609/24/201810, 11Exam 1, Peer Feedback09/30/2018
810/08/201813, 14Homework 210/14/2018
910/15/201815, 16Peer Feedback, Mid-Course Survey10/21/2018
1010/22/201817, 18Project 210/28/2018
1110/29/201819, 20Exam 2, Peer Feedback11/04/2018
1211/05/201821, 2211/11/2018
1311/12/201823, 24Homework 311/18/2018
1411/19/201825Peer Feedback11/25/2018
1511/26/2018Project 312/02/2018
1612/03/2018Exam 3, Peer Feedback12/09/2018
1712/10/201826End-of-Course Survey, CIOS Survey12/16/2018

Given above are the numeric labels for each lesson. For reference, here are those lessons’ titles, with the estimated time to complete each lesson in minutes in parentheses:

  • 01: Introduction to Knowledge-Based AI (45)
  • 02: Introduction to CS7637 (60)
  • 03: Semantic Networks (60)
  • 04: Generate & Test (30)
  • 05: Means-Ends Analysis (60)
  • 06: Production Systems (60)
  • 07: Frames (45)
  • 08: Learning by Recording Cases (30)
  • 09: Case-Based Reasoning (60)
  • 10: Incremental Concept Learning (60)
  • 11: Classification (45)
  • 12: Logic (90)
  • 13: Planning (75)
  • 14: Understanding (30)
  • 15: Commonsense Reasoning (60)
  • 16: Scripts (30)
  • 17: Explanation-Based Learning (45)
  • 18: Analogical Reasoning (60)
  • 19: Version Spaces (60)
  • 20: Constraint Propagation (45)
  • 21: Configuration (45)
  • 22: Diagnosis (45)
  • 23: Learning by Correcting Mistakes (45)
  • 24: Meta-Reasoning (30)
  • 25: Advanced Topics (60)
  • 26: Wrap-Up (30)

Course Assessments

Your grade in this class is generally made of four components: three homework assignments, three projects, three exams, and class participation.

Final grades will be calculated as an average of all individual grade components, weighted according to the percentages below. Students receiving a final average of 90 or above will receive an A; of 80 to 90 will receive a B; of 70 to 80 will receive a C; of 60 to 70 will receive a D; and of below 60 will receive an F. We do not plan to have a curve. It is intentionally possible for every student in the class to receive an A.

Homework (30%)

You will complete three homework assignments in this course, each worth 10% of your average. Each homework assignment will have four questions, which you will answer in around three pages each. These questions will cover the course material, the ethics of AI, and the depiction of AI in the media. You will be expected to do some outside research for some of these questions. All assignments should be written using JDF.

Projects (45%)

You will complete three projects in this course, each worth 15% of your average. Each project has two components: an implementation portion and a reflection portion. The implementation portion will be graded on the quality of your project’s execution and its performance according to some benchmarks or objective evaluations. The reflection portion will be graded based on your presentation of your work and how it operates, as well how well you tie your implementation to human cognition. These reflections should be written using JDF.

Exams (15%)

You will take three proctored exams in this class, each worth 5% of your average. Each exam is one hour long with up to 15 questions, all multiple-choice, multiple-correct with five choices and between 1 and 4 correct answers. Partial credit is awarded. Each exam will cover all lectures through the previous week (for example, Exam 1 covers lessons 01 through 09). All exams are open-book, open-note, open-internet: everything except live interaction with another person. The tests are proctored via Proctortrack.

Class Participation (10%)

One of the major strengths of large online classes it the way they allow students to have significant impact on their classmates’ experiences. As such, 10% of your class grade and 10% of the time you spend on this class will be improving the course experience for other students. This is participation credit, and it can be earned in four ways: through completion of peer reviews, through participation on Piazza, through completion of course surveys, and through completion of student-initiated activities. This is designed such that you can earn your full participation credit through peer review and course surveys alone, but if you are comfortable participating in other ways, it will count in your favor as well.

Course Policies

The following policies are binding for this course.

Official Course Communication

You are responsible for knowing the following information:

  1. Anything posted to this syllabus (including the pages linked from here, such as the general course landing page).
  2. Anything emailed directly to you by the teaching team (including announcements via Piazza), 24 hours after receiving such an email.

Because Piazza announcements are emailed to you as well, you need only to check your Georgia Tech email once every 24 hours to remain up-to-date on new information during the semester. Georgia Tech generally recommends students to check their Georgia Tech email once every 24 hours. So, if an announcement or message is time sensitive, you will not be responsible for the contents of the announcement until 24 hours after it has been sent.

We generally prefer to handle communication via Piazza to help with collaboration among the teaching team, but we understand Piazza is not ideal for having information “pushed” to you. We may contact you via a private Piazza post instead of an email, but if we do so, we will choose to send email notifications immediately, bypassing your individual settings, in order to ensure you’re alerted. As such, this type of communication will also spring under #2 above.

Note that this means you won’t be responsible for knowing information communicated in several other methods we’ll be using. You aren’t responsible for knowing anything posted to Piazza that isn’t linked from an official announcement. You aren’t responsible for anything said in Slack or other third-party sites we may sometimes use to communicate with students. You don’t need to worry about missing critical information so long as you keep up with your email and understand the documents on this web site. This also applies in reverse: we do not monitor or Canvas message boxes and we may not respond to direct emails. If you need to get in touch with the course staff, please post privately to Piazza (either to all Instructors or to an instructor individually) or tag the instructor in the relevant post.

Office Hours

This class uses the chat tool Slack for its office hours. Slack is a popular team communication chat tool that allows conversations in public rooms, private rooms, and private messages. You can sign up for the student Slack community at Slack office hours are not scheduled at specific times; instead, the instructor is usually available on Slack throughout the day and responds quickly. In general, you may ask questions in the public #office-hours room, or message him directly. When necessary, Webex, BlueJeans, or other forms of conversation can be launched from within Slack. If you are not comfortable signing up for Slack to participate in Slack office hours, you may also feel free to email or post privately on Piazza to set up a chat via an alternate technology.

Late Work

Running such a large class involves a detailed workflow for assigning assignments to graders, grading those assignments, and returning those grades. As such, work that does not enter into that workflow presents a major delay. Thus, we cannot accept any late work in this class. All assignments must be submitted by the posted deadlines. We have made the descriptions of all assignments available on the first day of class so that if there are expected interruptions (business trips, family vacations, etc.), you can complete the work ahead of time.

If you have technical difficulties submitting the assignment to Canvas, post privately to Piazza immediately and attach your submission. Then, submit it to Canvas as soon as you can thereafter.

If you have an emergency and absolutely cannot submit an assignment by the posted deadlines, we ask you to go through the Dean of Students’ office regarding class absences. The Dean of Students is equipped to address emergencies that we lack the resources to address. Additionally, the Dean of Students office can coordinate with you and alert all your classes together instead of requiring you to contact each professor individually. You may find information on contacting the Dean of Students with regard to personal emergencies here:

The Dean of Students is there to be an advocate and partner for you when you’re in a crisis; we wholeheartedly recommend taking advantage of this resource if you are in need. Justifiable excuses here would involve any major unforeseen disruption to your classwork, such as illnesses, injuries, deaths, and births, all for either you or your family. Note that for foreseen but unavoidable conflicts, like weddings, business trips, and conferences, you should complete your work in advance; this is why we have made sure to provide all assignment and project resources in advance. If you have such a conflict specifically with the tests, let us know and we’ll try to work with you.

Academic Honesty

All students in the class are expected to know and abide by the Georgia Tech Academic Honor Code. In general, we strongly encourage collaboration in this class. You are encouraged to discuss the course material, the exercises, the written assignments, and project with your classmates, both before and after assignments and projects are due. Similarly, we will be posting the best assignments for public viewing so you may learn from the success of others’ designs. However, we draw a firm line regarding what copying is permissible in your assignments. Specifically, you must adhere to the following rules:

  • Any content that is copied or barely paraphrased from existing literature must be cited, both in the references at the conclusion of your assignment and in-line where the borrowed material appears. Failing to provide in-line citations for borrowed material will be regarded as plagiarism even if the source is provided in the references. This applies to figures as well as text, including those figures that are part of this course’s material.
  • Do not copy any content from other students in current or previous semesters of KBAI, even if cited.

In all written work, sources should be cited in APA style, both in-line and at the end of the document. Please consult the Purdue OWL for information on when and how to cite sources in research. When in doubt, don’t hesitate to ask!

Regarding the course’s proctored exams, you are permitted to consult any resource except live engagement with another human being. You should not email, post on forums, chat, text, or discuss with anyone physically with you during any exam.

Any violations of this policy may be subject to the institute’s Academic Integrity procedures, which may include a 0 grade on assignments found to contain violations; additional grade penalties; academic probation or dismissal; and prohibition from withdrawing from the class.


This is the most substantial redesign of CS7637: Knowledge-Based AI since the class started in Fall 2014. We ask your patience and support as we figure things out, and in return, we promise that we, too, will be fair and understanding, especially with anything that might impact your grade or performance in the class. Second, we want to consistently get feedback on how we can improve and expand the course for future iterations. You can take advantage of the feedback box on Piazza (especially if you want to gather input from others in the class), give us feedback on the surveys, or contact us directly via private Piazza messages.