|
Call for Papers |
Schedule |
Papers |
Statements |
Organizers |
Programme committe |
As computer systems become more powerful and complex, our
interactions with them have become more information laden and,
consequently, more burdensome. It is now generally recognized within
the HCI and intelligent user interfaces communities that as systems
become more complex, this need for higher-bandwidth interfaces should
be addressed by learning about and adapting to the user. The pieces to
this puzzle are coming together from a variety of disciplines,
including machine learning, user modeling, intelligent tutoring,
information retrieval, and data mining. Furthermore, related work is
discussed in the field of autonomous agents. This workshop aims at
bringing together researchers from these different communities.
The goal of the workshop is to make a first step towards a
framework within which research on systems that adapt to their users
can be proposed, identified, conducted and evaluated. In the call for
papers, we identified the following technical issues to be discussed
in submitted papers and during the workshop itself:
- Problem Domain:
- What is the task?
- What is unique about the task and why is it important?
- What will a solution in this domain tell us about general solutions?
- Approach or Method
- How was data (from which to learn) collected?
- What learning algorithm was used?
- Was learning on-line or off-line?
- How was the learned model utilized?
- Evaluation
- How do you measure success on the overall task?
- How do you measure improvement for a given user?
- What were the causes for success or failure?
Schedule
| 8.30- 9.00
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Introduction, presentation of participants
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| 9.00-10.00
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Session 1 - Formal Methods: Lane, Grossmann-Hutter
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| 10.00-10.30
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Coffee break
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| 10.30-12.00
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Session 2 - User Plans/Tasks: Bauer, Paynter, Ruvini
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| 12.00-13.00
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Session 3 - Evaluation: Zukerman, Zia
|
| 13.00-14.00
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Lunch
|
| 14.00-15.30
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Session 4 - Specific Information Needs: Basu, Goeker, Macskassy
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| 15.30-16.00
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Coffee break
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| 16.00-17.00
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Session 5 - General Issues: Pohl, Delgado
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| 17.00-18.00
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General discussion
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| 19.00 -
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Informal workshop dinner (not included in workshop fee)
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Workshop papers
- Chumki Basu, Haym Hirsh, William W. Cohen, and Craig Nevill-Manning: Recommending Papers by Mining the Web
PS
- Mathias Bauer: Generation of Alternative Decompositions for Plan Libraries
PS
- Delgado, Joaquin and Ishii, Naohiro: Formal Models for Learning User Preferences, A Preliminary Report
Zipped PS
- Ayse Göker: Capturing Information Need by Learning User Context
PS
- Barbara Großmann-Hutter, Anthony Jameson, Frank Wittig: Learning Bayesian Networks With Hidden Variables for User Modeling
PDF,
Slides
- Terran Lane: Hidden Markov Models for Human/Computer Interface Modeling
PS
- Sofus A. Macskassy, Aynur A. Dayanik, and Haym Hirsh: EmailValet: Learning Email Preferences for Wireless Platforms
PS
- Gordon W. Paynter and Ian H. Witten: Automating Iteration with Programming by Demonstration: Learning the Users Task
PS
- Wolfgang Pohl, Ingo Schwab, and Ivan Koychev: Learning About the User: A General Approach and its Application
PS
- Jean-David Ruvini and Christophe Dony: Learning Users Habits: The APE Project
PS,
Zipped PS
- Hassan Zia, Qaiser S. Durrani, Rana Adnan Farrakh, Amir Riaz, and Farhan Ahmed: CITS - C++ Intelligent Tutoring System: A Domain Independent User Centered Curriculum Approach
MS Word
- I. Zukerman, A.E. Nicholson, and D.W. Albrecht: Evaluation Methods for Learning about Users
PS
Statements of interest
- Pedro Domingos, Instituto Superior Tecnico, Lisbon, Portugal
TXT
- Banu Raskutti,
TXT
- Joel Ratsaby, Manna Network Technologies, Inc, Tel Aviv, Israel
HTML
Organizing Committee
-
Åsa Rudström (chair),
Swedish Institute of Computer Science (SICS), Sweden,
asa@sics.se
- Dr. Mathias Bauer,
German Research Center for AI (DFKI), Germany ,
bauer@dfki.de
- Dr. Wayne Iba,
Computational Learning Laboratory, Stanford University, USA,
iba@apres.stanford.edu
- Dr. Wolfgang Pohl,
GMD FIT, HCI Department, Germany ,
Wolfgang.Pohl@gmd.de
Program Committee
- Haym Hirsh, Rutgers University, USA
- Henry Lieberman, MIT Media Lab, USA
- Katharina Morik, University of Dortmund, Germany
- Hiroshi Motoda, Osaka University, Japan
- Geoff Webb, Deakin University, Australia
