AI代写|OMS CS7637: Knowledge-Based AI — Spring 2022



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 Spring 2022 OMS CS7637 class. Note that assignment due dates are all Sundays at 11:59PM Anywhere on Earth time.

Week # Week Of Lessons Deliverable Assignment Due Date
1 01/10/2022 01, 02 Introductions, Start-of-Course Survey 01/16/2022
2 01/17/2022 03, 04 RPM Milestone 1 01/23/2022
3 01/24/2022 05, 06 Mini-Project 1 01/30/2022
4 01/31/2022 07, 08 Homework 1 02/06/2022
5 02/07/2022 09 Mini-Project 2, Quarter-Course Survey 02/13/2022
6 02/14/2022 10, 11 RPM Milestone 2 02/20/2022
7 02/21/2022 12 Exam 1 02/27/2022
8 02/28/2022 13, 14 Homework 2 03/06/2022
9 03/07/2022 15, 16 Mini-Project 3, Mid-Course Survey 03/13/2022
10 03/14/2022 17, 18 RPM Milestone 3 03/20/2022
11 03/21/2022 19, 20 Mini-Project 4 03/27/2022
12 03/28/2022 21, 22 Homework 3 04/03/2022
13 04/04/2022 23, 24 Mini-Project 5 04/10/2022
14 04/11/2022 25 RPM Milestone 4 04/17/2022
15 04/18/2022 Final RPM Project 04/24/2022
16 04/25/2022 Exam 2 05/01/2022
17 05/02/2022 26 End-of-Course Survey, CIOS Survey 05/08/2022

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 five components: three homework assignments, five mini-projects, one large project, two 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 (15%)

You will complete three homework assignments in this course, each worth 5% of your average. Each homework assignment will have two questions, which you will answer in around three pages each. These questions will cover the course material, as well as give you a chance to investigate cutting-edge AI research. You will be expected to do some outside research for some of these questions. All assignments should be written using JDF.

Mini-Projects (30%)

You will complete five mini-projects in this course, each worth 6% of your average. Each mini-project asks you to implement some AI logic shown in the course lectures, although you are also welcome to attempt to solve the problems using other techniques. For each of the mini-projects, you will also provide a short write-up of your approach, mainly to share with classmates and look through others’ approaches. These write-ups should be written using JDF. You’ll submit the write-ups to Canvas and the code to Gradescope.

Raven’s Project Milestones (15%) and Raven’s Final Project (15%)

The semester-long project is the Raven’s project, where you will write an agent that can solve problems on the Raven’s Progressive Matrices test. For the project, you will complete four milestones throughout the semester, and then a final submission. The four milestones together are worth 15% of your average, and the final submission is worth another 15%. The milestones are there to ensure that you get started on the project early and have an opportunity to see your classmates’ approaches. Each milestone, as well as the final project submission, is graded half on performance and half on a written report. These write-ups should be written using JDF. You’ll submit the write-ups to Canvas and the code to Gradescope.

Exams (15%)

You will take two proctored exams in this class, each worth 7.5% of your average. Each exam is 90 minutes long with up to 25 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 current week (for example, Exam 1 covers lessons 01 through 12). All exams are open-book, open-note, open-internet: everything except live interaction with another person. The tests are digitally proctored. Tests will open at least one week prior to the deadline, though they may be open earlier.

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 various ways: participating on the class forum; participating in peer review; submitting annotated bibliographies for the course resources; submitting candidate exam questions; participating in other activities; completing course surveys; completing the secret survey by clicking the hidden link here before the end of week 2 to indicate you read the entire syllabus; and more. There may be other mechanisms to earn participation points announced throughout the semester; check the course forum for that!

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 the course forum or Canvas), 24 hours after receiving such an email.

Generally speaking, we will post announcements via Canvas and cross-post their content to the course forum; you should thus ensure that your Canvas settings are such that you receive these announcements promptly, ideally via email (in addition to other mechanisms if you’d like). 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 the course forum to help with collaboration among the teaching team, but we understand the course forum is not ideal for having information “pushed” to you. We may contact you via a private the course forum 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 the course forum 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 the course forum (either to all Instructors or to an instructor individually) or tag the instructor in the relevant post.

Communicating with Instructors and TAs

Communication with the course teaching team should be handled via the discussion forum. If your question is relevant to the entire class, you should ask it publicly; if your question is specific to you, such as a question about your specific grade or submission, you should ask it privately.

Our workflow is to regularly filter the forum for Unresolved posts, which includes top-level threads with no answer accepted by the original poster, as well as mega-threads with unresolved follow-ups. If your question requires an official answer or follow-up from an instructor or teaching assistant, make sure that it is posted as either a Question or as a follow-up to a mega-thread, and that it is marked Unresolved. Once an instructor or TA has answered your question, it will automatically be marked as Resolved; if you require further assistance, you are welcome to add a follow-up, but make sure to unmark the question as Resolved in order to make sure that it is seen by a member of the teaching team.

Similarly, in order to keep the forum organized, please post as a Post or Note instead of a Question if your question does not require an official response from the teaching team. For example, if you are interested in getting multiple perspective from classmates, getting feedback on your ideas, or having a discussion that does not have a single answer, please use Post or Note instead of Question. Please reserve Question threads for questions that will likely have a single official response. TAs and instructors will regularly convert Questions to Posts or Notes that do not need a single official answer, but it will save time and allow them to focus their attention on other students if you correctly categorize your post in the first place.

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. We have taken steps to limit as much as possible the need to ever submit work late: we have made the descriptions of all assignments available on the first day of class so that if there are expected interruptions (such as like weddings, business trips, and conferences), you can complete the work ahead of time. If you have technical difficulties submitting the assignment to Canvas by the deadline, post privately to the course forum immediately and attach your submission. Then, submit it to Canvas as soon as you can thereafter.

If due to a personal emergency, health emergency, family emergency, or other unforeseeable life event you find you are unable to complete an assignment on time, please post privately to the course forum with information regarding the emergency. Depending on your unique situation, we will share guidance on how to proceed; if the emergency is projected to delay a significant quantity of the work required for the class, we may recommend withdrawing and reattempting the class at a later date. If the emergency will likely only impact a small amount of the course, we may be able to accept the work late as a one-time exception. If the emergency takes place once you have already completed a significant fraction of the coursework, we may offer an Incomplete grade to allow you to finish the class after the semester is over.