ACM Home Page
Please provide us with feedback. Feedback
From geek to sleek: integrating task learning tools to support end users in real-world applications
Full text PdfPdf (554 KB)
Source
International Conference on Intelligent User Interfaces archive
Proceedings of the 13th international conference on Intelligent user interfaces table of contents
Sanibel Island, Florida, USA
SESSION: Short papers table of contents
Pages 389-394  
Year of Publication: 2009
ISBN:978-1-60558-168-2
Authors
Aaron Spaulding  SRI International, Menlo Park, CA, USA
Jim Blythe  University of Southern California, Marina del Rey, CA, USA
Will Haines  SRI International, Menlo Park, CA, USA
Melinda Gervasio  SRI International, Menlo Park, CA, USA
Sponsors
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 6,   Downloads (12 Months): 82,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1502650.1502706
What is a DOI?

ABSTRACT

Numerous techniques exist to help users automate repetitive tasks; however, none of these methods fully support end-user creation, use, and modification of the learned tasks. We present an integrated task learning system (ITL) that learns executable procedures based on user demonstration and instruction, constituting a first step toward a broader solution for procedure management. We discuss our deployment of ITL into a collaborative command-and-control system. In this complex domain, ITL's performance with end users doing real tasks indicates that providing multiple, integrated learning techniques both extends functionality and improves user experience. Our experience in integrat-ing this system also provides key insights for future designs of domain-independent task learning systems, specifically in supporting users' ability to understand and edit lengthy procedures.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Allen, J. et al. PLOW: A Collaborative Task Learning Agent. Proc. AAAI 2007.
2
 
3
Burnett, M. Software Engineering for Visual Programming Languages. Handbook of Software Engineering and Knowledge Engineering, Vol. 2, World Scientific Publishing Company, June 2001.
 
4
Burstein, M., Laddaga, R., McDonald, D., Benyo, B., et al. POIROT--Integrated Learning of Web Service Procedures. Proc. AAAI-08.
 
5
Command Post of the Future (CPOF) http://www.darpa.gov/sto/strategic/cpof.html
 
6
7
 
8
Gervasio, M., Lee, T. J., and Eker, S. Learning Email Procedures for the Desktop. Proc. AAAI 2008 Workshop on Enhanced Messaging.
 
9
Huffman, S. and Laird, J. Flexibly Instructable Agents, Journal of AI Research, 3, 1995
 
10
Lerman, K., Plangrasopchok, A. and Knoblock, C. Semantic Labelling of Online Information Sources, IJSWIS 2007.
 
11
Lieberman, H. (Ed.). Your Wish is My Command: Programming by Example. Morgan Kaufmann, San Francisco, CA, 2001.
 
12
 
13

Collaborative Colleagues:
Aaron Spaulding: colleagues
Jim Blythe: colleagues
Will Haines: colleagues
Melinda Gervasio: colleagues