MQPs 2003-2004

Prof. David C. Brown, CS Dept., WPI

* * * 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 am the CS coordinator for MQPs at NASA Goddard Space Flight Center. Typically two CS MQPs are done there per year, 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

  • WPI/NASA GSFC projects:

    • DCB-03N1
    • Title: Creating a context management system
    • NASA Mentor / Contact: Mary Reph <Mary.G.Reph@nasa.gov>
      WPI Faculty Advisor: Prof. Dave Brown
      Students: Jeff Bacon <psyci@WPI.EDU>, Nicholas Pinney <lutris@WPI.EDU>, John waymouth <waymouth@WPI.EDU>
      -- A term 2003
      NASA Location: tbd;

    • DCB-03N2
    • Title: Programming support for the Spectral Analysis Automation project
    • NASA Mentor / Contact: Walt Truszkowski <Walter.F.Truszkowski@nasa.gov>
      WPI Faculty Advisor: Prof. Dave Brown
      Students: Chris Aniszcsyk <zx@WPI.EDU>, Scot Junkin <sjunkin@WPI.EDU>, Owen Smith <ender3rd@WPI.EDU>
      -- A term 2003
      NASA Location: tbd;

    • DCB-03N3
    • Title: Real-time interface software for GPS receivers
    • NASA Mentor / Contact: Jesse Leitner <Jesse.A.Leitner@nasa.gov>
      WPI Faculty Advisors: Profs. Dave Brown & Fred Looft
      Students: Michael Andren <mandren@WPI.EDU>, Jacob Castiglione <jacobc@WPI.EDU>, John Woodhull <jc23@WPI.EDU>
      -- A term 2003
      NASA Location: tbd;

  • (DCB-0301) 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-0302) 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-0303) 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-0304) Adaptive Web Sites

    • (DCB-0305) 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-0306) 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-0307) 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-0308) 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-0309) 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-0310) 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-0311) 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-0312) Generalization of specific design traces into a methodology

  • (DCB-0313) 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?

 

Recent Projects

  • (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++
  • 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

 

Additional SiFA Project

It would be useful to have a system that draws diagrams of the interconnections between SiFAs and animates the diagram (perhaps by changing colors) as the SiFAs conflict (e.g., red), negotiate (e.g, amber) and resolve (e.g., green) their differences.

dcb@cs.wpi.edu / Sun, 9 Feb 2003 20:04:46