Projects (Overview with Links to Details) |
CSE 415: Introduction to Artificial Intelligence The University of Washington, Seattle, Winter 2018 |
The last assignment in the course is called the project, because it
differs from the earlier assignments in three respects:
(a) there is more flexibility in the topics; there are three options
instead of one, and within each option there is some flexibility
in terms of techniques involved and implementations;
(b) the project is done in partnerships of two people;
(c) although the implementation is important, there is also a
nontrivial written report to turn in.
Whereas earlier assignments focused primarily on specific techniques and coding, the project offers an opportunity to think more broadly about building AI agents or systems, to collaborate and communicate with someone about the issues, and to be more creative in the design of the experiment, application or agent. |
OPTION 1: Baroque Chess Agent. This option is for those who wish to further explore the design of game-playing agents. It builds on Assignment 4 by taking the issues of minimax search and its various optimizations to a game with a more interesting set of rules. Unlike OPTIONS 2 and 3, we plan to have a tournament among the OPTION 1 agents. There is starter code for this option. Click here for more information. The lead TA for this option is Yue Zhang. |
OPTION 2: Feature-Based Reinforcement Learning for the Rubik Cube Puzzle. This option involves applying reinforcement learning to a problem whose state space is so large that it requires that states be represented in terms of a much more limited set of patterns or features (and therefore in groups of states) rather than their full details (and individually). The feature-based approach is important in practice. However, the Rubik Cube puzzle is sufficiently easy to understand that it grounds this option in something familiar. Click here for more information. The lead TA for this option is Bindita Chaudhuri. |
OPTION 3: Supervised Learning: Comparing Trainable Classifiers. In this option, a team will implement two trainable classifiers and then compare their performance using two separate data sets and alternative training policies. The suggested types of classifiers are: (a) Random forests (of decision trees), (b) Two-layer feedforward neural networks, and (c) K-nearest neighbors. In addition to using these two classifiers separately, combine them by using either bagging or boosting, and describe any improvement in performance that results from combining them. Click here for more information. The lead TA for this option is Emilia Gan. |
Partnerships. Partnerships are strongly encouraged but not required. Besides the advantages of a partnership in terms of sharing work, gaining experience with collaboration and communication, and probably having more fun, partnerships will be eligible for the extra credit section of the report, in which each student describes the partnership experience within the project. |
Milestones. There are three separate turn-in deadlines for the
projects.
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Criteria.
Each project should address the following criteria.
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Demonstrations.
Project presentations are currently scheduled to take place during class on March 7 and March 9. Each project should be shown on both these days. During the demonstration periods, each student should complete two peer evaluations on each day. Printed copies of the forms will be available in class. Each team should bring a laptop to class that day in order to demo your project to small groups. We might spend part of each period having a few volunteer teams present to the whole class, but most of the time will be spent with multiple presentations to small groups happening simultaneously. |
What to Turn In.
Turn in the following files (zipped up or tarred) by the Milestone 3 deadline. (Late days are not allowed in the project.) Report.pdf; code files; data files if doing Option 3. |
Reports
The report should be an electronic document in the form of a PDF file. The key elements of your report include:
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If required, further details will be announced later. |