Projects
CSE 415: Introduction to Artificial Intelligence
The University of Washington, Seattle, Winter 2007
Projects are an opportunity to invent your own artificially intelligent program that performs an activity that you specify.
Projects can be of any of the following four types:
  • Problem-solving systems based on T-Star. These systems will help users solve traditional problems such as scheduling, spatial layout (floor plans), combinatorial arrangement (pentaminoes), graph coloring, graph isomorphism. Choose a class of problems and implement state/node representations and renderings, basic operators, and automatic search and evaluation functions.
  • Expert agents written in Python. Such an agent will reason about problems in a specific domains such as an area of science (e.g., chemical reactions), forensics (crime-scene evidence), mathematics, law, business, etc. Rule based and probabilistic reasoning are appropriate mechanisms to employ.
  • Demonstration and application of a standard AI technique using Python + TKinter. For example, the ID3 learning algorithm for classification rules, concept formation in mathematics in the spirit of AM or Pythagoras: "Primes = Natural numbers such that the number of factors is minimized". (If you plan to use the Stone World code in your project, it is here: StoneWorld.py, and the helper module Match2.py.)
  • Image understanding applications using PixelMath. Detection or recognition of: faces, objects, drawings, captchas. Watermark embedding and extraction. Intelligence test problem-solving ("A is to B as C is to ...").
    • Projects may be done in teams of 1, 2 or 3 people. Further details to be announced.
    The theme for the projects is "Collaborative Problem-Solving Systems". This is meant to suggest that each program implement one or more "problem-solving assistants". These assistants may work in collaboration with the user, or with each other, to solve problems.
    Project demonstrations will take place Friday, March 9 in our lab. Each student must perform 2 peer evaluations of projects of others. Each project must be evaluated by at least two students not on the project team. All evaluations must be signed by evaluator, evaluatees, and must be turned in. Copies of the form were given out in class on March 7, and more copies will be available at the demo session. The form is also available here.
    Reports and source code are due Monday, March 12 at 5:00 PM. Use E-Submit to turn them in.
    Reports
    The report should be an electronic document (either raw ASCII text, MS Word file, or a PDF file). The key elements of your report include:
    1. title of project;
    2. names and roles of each teammate;
    3. what the program is supposed to do;
    4. technique used and brief description (half a page) of how that technique works. If you use multiple AI techniques then describe each one but with somewhat less detail for each one;
    5. either a screen shot or a transcript of an interesting sample session;
    6. brief demo instructions;
    7. code excerpt showing some interesting part(s) of your Python code and some explanation of it;
    8. brief description of what each team member learned in this project;
    9. what you would like to add to your program if you had more time;
    10. citations for any references you used in the project.
    11. As an "appendix" to the project, provide full source code for your program.