CS 497: Planning and Decision Making
Information on Projects:
Each student will be asked to submit a project. A substantial amount
of flexibility exists in the kind of project that can be done. The
main criteria for grading will be: 1) level of originality,
creativity, cleverness, etc., 2) relevance to the theme of the course,
3) general difficulty, amount of effort, etc., 4) quality of the
written account of the project (which could exist as a web page,
ps/pdf file, or written document).
Here are some examples of the kinds of projects, to help give you
- Carefully study frequentist decision theory, and use this
knowledge to make a frequentist version of something covered in the
first part of the class or in one of the papers. For example, can
define a sequential decision making scenario, and apply dynamic
programming to find optimal solutions? Another possibility would be
to implement frequentist decision rules and design experiments that
illustrate the tradeoffs and issues between Bayesian and frequentist
- Implement and experimentally evaluate an algorithm from one of the
papers. Can you identify strengths and weaknesses of the approach?
- Attempt to extend or improve an algorithm given in the first part
of the class or in a paper.
- Define a variant of a model considered in one of the papers, and
attempt to adapt the tools and techniques to apply to your variant.
- For a more-specific idea, Pareto optimality was covered, and also
sequential decision making for problems other than multiobjective
optimization. Can you take dynamic programming principles that were
designed for single objectives and extend them to multiple objectives?
For example, can you design an algorithm that behaves in a way similar
to Dijkstra's algorithm for graph search, but instead yields all
minimal solutions with respect to the partial order defined in class
for multiobjective optimization problems? Can you analyze the running
time of such an algorithm?
- For a given scenario, possibly taken from a paper, attempt to
perform sensitivity analysis, either experimentally, or by proving
bounds on the input parameters (e.g., prior distributions).