Projects (DRAFT VERSION)
CSE 415: Introduction to Artificial Intelligence
The University of Washington, Seattle, Autumn 2012
Projects are an opportunity to invent your own artificially intelligent program that performs an activity that you specify.
Types of Projects
Projects can be of any of the following five 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. The T-Star software is available at the T-Star website.
  • Collaborative problem-solving systems based on CoSolve. Using a recently-developed web-based environment for state-space search called CoSolve, design and implement a state representation scheme and set of operators for a new class of problems. Take advantage of the possibility of having teams of solvers apply your operators. Two types of problems are possible: (a) standard puzzles and/or games, e.g., pentaminos, Travelling-Salesman Problem, Chess. These are appropriate for one-person teams; (b) "wicked" problems (e.g., climate change): part of your contribution will be to interpret the problem and model some aspect of it, possibly coming up with an original puzzle or game. These are appropriate for two-person teams. To consider the CoSolve option further, go to the CoSolve website on Socrates and create an account for yourself. Then, browse through some of the existing problem templates and solving sessions. If you decide to do a CoSolve project, then the staff will arrange for you to get poser privileges on the site.
  • Demonstration and application of a standard AI technique using Python + TKinter. For example, Support Vector machines for nonlinear classification, 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 ...").
    • Teams
    Projects may be done in teams of 1 or 2 people.
    Meetings with Instructor
    Each team should discuss their project with the instructor.
    Demonstrations
    Project presentations will currently scheduled to take place during December 3 and 5 in the last week of classes. During this time, each student should complete two peer evaluations on each day. Printed copies of the forms will be available at the lab.
    Criteria
    Each project should address the following criteria.
    (a) Originality. The project must not be an edited version of something developed elsewhere.
    (b) Demonstration of an appropriate artificial intelligence technique. Most AI topics will be appropriate, but each student or team should meet with the instructor and get approval for their topic.
    (c) Transparency. The implementation should take specific steps or incorporate explicit features to help the user understand how the program works, typically showing in some form, key intermediate results, status of longer computations, and relationships among key data structures, rules, states, or constraints.
    (d) Interactivity. The program should allow the user to control the computation through interaction. There can be default parameter settings, but it must be possible for the user to easily override many of the defaults.
    Due dates
    Reports and source code are due Wednesday, December 12 at 11:45 PM. Use Catalyst Collect-It 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.
    Fill out the project plan questionnaire by Thursday, November 29 at 5:00 PM.