CMSC 601 Topic Paragraph Guidelines

Due Tuesday, February 10

Requirements

Identifying an area that is neither too broad ("distributed networking") nor too narrow ("minimum energy routing protocols for distributed networking") should be your primary goal at this point of your project.  Idealy, you should be focusing on problems ("efficient distributed routing protocols") rather than methods.  Focusing on methods limits your scope -- the existing methods to solve a problem are what's already been done; to do research, you need to be able to set your mind free to think about what could be done.

Many areas ("preference elicitation") turn out to be much broader in scope that they initially seem to be, especially when you start branching out into related areas.  As you work through the background-reading process on the way to a literature survey, you'll continually go through "expansion and contraction" cycles: expansion as you identify new related areas, and contraction as you come to understand which sub-problems you particularly want to focus on.  In your literature survey, you'll likely have pointers into several relevant bodies of research that are related in interesting ways to your work, but are not directly feeding into your ideas.  When you do this, you need to explain both why the area is related to your line of investigation, and why it isn't so directly relevant as to require an extensive survey.

Your topic paragraph (which is due on Tuesday, February 10) should follow the following format:

Below is an example paragraph, with the necessary elements.
 

Example Paragraph

Topic: Artificial intelligence: Preference elicitation

Description: Methods for querying a user about their preferences over an outcome space, in order to elicit the preference structure they have over attributes of possible outcomes.

Areas/keywords: Medical decision making, decision theory/utility theory, preference learning/utility learning, multiattribute decision theory, structured representations for utilities

Relevant publications:

Papers: Key Names:  Craig Boutilier, Fahiem Bacchus, Jon Doyle, Michael Wellman

Reader: Not identified yet.