MQPs 2006-2007
Prof. David C. Brown, CS Dept., WPI
Summary of Project Interests
* * * Under Construction * * *
*** Note that some of these undergraduate projects might easily be
expanded to Master's level research.
General
I am interested in working with smart, diligent, independent students on
projects concerning HCI, and projects relating AI to HCI or to Design
(software or engineering).
I am open to any proposals from students, in particular to work on
testing work developed by MS and PhD students in the area of the
application of AI to Design, especially
in design rationale, functional reasoning, simplification or
multi-agent systems (e.g., SiFAs).
These require AI knowledge such as that obtained in CS 4341, or CS 534.
I do one or two projects a year in conjunction with a scientist at
NASA Goddard Space Flight Center, usually with teams of three people.
Note that I follow most of the
Project Guidelines
described by Prof. Wills.
Please look at my description of
grading standards before starting a project.
For many projects the Research
Methods Knowledge Base should be very useful.
Please look at
Collected Information about Writing, and
Hints For Writing Theses or Project Reports
in particular.
Proposed & Active MQP Projects
- NASA GSFC project:
- DCB-06N1
- Title: Semantic Desktop
- NASA Contact: Walt Truszkowski <Walt.Truszkowski@nasa.gov>
Students: Emily Potter ,
Jason Brooks
-- A term 2006
NASA Location: tbd;
(DCB-0601) Continuing studies of Machine Learning in the simulated Soccer
Modelling soccer teams with groups of intelligent programs,
called Agents, is an ongoing area of research in AI. How do we
model soccer skill? How do we model what players know about
teamwork, about cooperation? How can we reward simulated players
for playing well? How can teams learn from instructions?
HCI-related projects:
- (DCB-0602) Web-based cooperative work projects
What happens if you give a team of people who are
solving a problem, or writing a proposal, or commenting on a
class, read and write access to the same web page? Do they
change the way they work? Does the quality of their work
change? What tools do they need to keep track of what they
are doing together?
- (DCB-0603) Intelligent Interfaces
Intelligent Interfaces are those that act
intelligently in some way while interacting with a
user. They might interpret a user's request into lower level
actions that together will do what the user wants, or they
might present information to the user in a customized manner.
Often they involve modelling the user in some way.
- (DCB-0604) Adaptive Web Sites
Adaptive web sites are web sites that automatically improve
their organization and presentation by learning from user
access patterns. This has mostly been done for commercial
reasons so far. However, what sorts of changes are possible?
What effects can they have? How do the users feel about
such changes? Do the changes affect their work? What
happens if combinations of small changes are made?
- (DCB-0605) Adapting Web Sites with GAs
Students: tbd
One way to implement Adaptive Web Sites is to use
Genetic Algorithms (GAs) to do the adaptation
Imagine that
we are interested in changing a web site, and that user
traces are stored that recorded web page access patterns for
the site. One could adapt the web site, say once a day, by
doing the following:
- Have/generate a `population' of descriptions of
alternative web site organizations (i.e., adaptations of
the current site).
- Develop a metric, such as path length, bytes to be
transferred, or some combination, for use as part of the
a fitness function.
- Develop a way of doing crossovers such that new, valid
site organization descriptions result.
- Run the GA mechanism on the population, evaluating the
fitness of the members of the population by using the
metric across all, a sample of, or a generalization of,
the recorded access patterns for the site.
- Better members of the population make more of the users
happy by lowering the metric.
- The `best' member becomes the new web site.
- (DCB-0606) Curious Browsers (with Prof. Claypool)
A problem that Adaptive Web Sites share with Information
Filtering and Recommendation Systems is how to determine the
amount of approval/interest the user of some information
(e.g., on a web site) is displaying about that information.
A simple method is to assume that accessing a page
indicates interest, just because they followed the link. To
be an accurate predictor of interest we have to know that
the text of the link is a perfect summary of the web page --
i.e., it contains the same information. This isn't
likely. Other indications might be time spent looking.
However, that might be due to confusion, or to answering the
phone.
Projects that might be done under this title might investigate:
link content vs. page content comparisons, and links as
predictors of interestingness; time access patterns and
distributions; explicit user-activated indicators of
interest (e.g., gauges); using the cursor in the browser
window as an indicator of the region of interest (e.g., by
detecting cursor movement or position); the effect of the
match between the user's task and web page `intended
function'; the effect of page familiarity on implicit indicators.
- (DCB-0607) Multi-modal interaction
This area studies how interaction in different modes
(e.g., speech, vision, keyboard) can be integrated, and how
that affects the users. Can new tasks be done that couldn't
be tackled before?
- (DCB-0608) Tools to support interface evaluation
In HCI we need to be able to carefully evaluate
interfaces. For a particular interface design we need to
collect enough data about the user's interaction so that
some analysis can be made. Therefore tools are needed for
automated protocol (textual & visual) capture.
- (DCB-0609) Web Page Evaluators (with Prof. Claypool)
Information filtering and recommendation systems are
being built to provide people with the information they
want to see. This is sorely needed when dealing
with the World Wide Web. One way of recommending a web page
to user A is to see which other user (e.g., user B) likes
all the same things. If user B likes something then user A
ought to be told. What's happening is that user B is
"evaluating" the web page using a variety of criteria, such
as quality of layout, clarity of the language, etcetera, in
addition to the content. If a cluster of software agents
could be built that make different kinds of evaluations
(e.g., more than 90% of the words used are under 6
characters long) then perhaps we dont need other users. This
project would build a variety of such agents, and test them
to see how predictive they were. i.e., if agent B likes it
does user A like it too?
`AI in Design' projects
- (DCB-0610) Adding Rationale Capture to a design tool
Software tools are available that help us during the
design of programs or engineered devices. e.g., a software
engineering tool, or a solid modeler. What's missing from
most design aiding systems, and hence also from design
documentation, is why each design decision was
made. This project is concerned with augmenting such tools
with both design rationale collection and rationale
inference.
A second version (project HXA-9940), in cooperation with Prof.
Ault (ME), was done with an ME student plus a CS
student. This project was concerned with
mechanical engineering design, and tried to augment a solid
modeler so that design rational could be captured. It could also have been
augmented using simple AI techniques in order to infer the rationale
from smaller modelling actions.
(DCB-0611) Knowledge Acquisition projects
AI systems, especially those that simulate expert
reasoning, use lots of knowledge. The process of `extracting'
that knowledge from a person is often called Knowledge
Acquisition. There are a wide variety of approaches, each with
particular uses. There has been less work on investigating how
they might be combined.
(DCB-0612) Generalization of specific design traces into a methodology
(DCB-0613) Under Karl Sim's Influence
Genetic Algorithms have been used to produce designs for bridges, cranes, programs,
and "living things". Their fitness is determined by placing them in a
simulated world to see how well they do. Sims produced "creatures" that swam
and crawled. Koza has produced programs. At Brandeis they're building cranes.
What can we produce?
(DCB-0614) Folksonomies: A Poor-Man's Taxonomy
- How it works
- Advantages & disadvantages
- New applications
- Connection to provided taxonomies
- Use in ontology extraction
- Folksonomy
- del.icio.us
- Flickr
- (DCB-News) News Release Database for Hometown Program
Students: Ralph Thompson & Andy Stone
- (DCB-0004) Adaptive Web Sites (with Prof. Cruz)
Students: Joel Minski & Esteban Burbano
- (DCB-0013) Human Computer Interaction for a Wearable Computer
Student: Brad Snow
- Curious Browsers
- P.Le & M.Waseda
- Instrumenting web browser to collect interest indicators.
- Skills: HTML, Visual Basic, HCI.
- The HTML Critic
- M.Bunar & P.Hynes,
- Providing an expert critique of a given web page.
- Skills: HTML, C, HCI.
- Teaching a Computer to Play Soccer
- J.Chausse,
- Applying reinforcement learning to a soccer simulation.
- Skills: AI, C++, HCI
-
Intelligent Agents in Newspaper Layout
-
D.Koelle,
- An agent based system to lay out newspapers articles.
- Skills: AI, HCI, C++
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- Adding Rationale Capture to a design tool
- A.Mossey & M.Luchini
- Augmenting CADKEY to collect design rationale.
- Skills: C, C++, AI, HCI, CAD
- Vietnam Business Directory
- Hiep Nguyen
- Setting up a web server and pages in Vietnam.
- Skills: HTML, perl, C, HCI.
- SiFAKA:
SiFA Knowledge Acquisition
- Mark Santesson (ME/CS)
- Extend the SiFAKA System and/or build a large SiFA system.
- Skills: Motif, C, HCI, AI.
- SiFAKA: SiFA Knowledge Acquisition
- Trish Currier (CS)
- Build a Motif system to acquire knowledge to automatically.
build multi-agent systems.
- Skills: AI, HCI, C, Motif
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