Article

Learning hierarchical task models by defining and refining examples

Authors:

Andrew Garland,

Charles RichAuthors Info & Claims

K-CAP '01: Proceedings of the 1st international conference on Knowledge capture

Pages 44 - 51

https://doi.org/10.1145/500737.500748

Published: 22 October 2001 Publication History

Abstract

Task models are used in many areas of computer science including planning, intelligent tutoring, plan recognition, interface design, and decision theory. However, developing task models is a significant practical challenge. We present a task model development environment centered around a machine learning engine that infers task models from examples. A novel aspect of the environment is support for a domain expert to refine past examples as he or she develops a clearer understanding of how to model the domain. Collectively, these examples constitute a "test suite" that the development environment manages in order to verify that changes to the evolving task model do not have unintended consequences.

References

[1]

R. Angros Jr. Learning What to Instruct: Acquiring Knowledge from Demonstrations and and Focussed Experimentation. PhD thesis, University of Southern California, 2000.]]

Digital Library

[2]

M. Bauer. Acquisition of Abstract Plan Descriptions for Plan Recognition. In Proc. 15th Nat. Conf. AI, pages 936-941, 1998.]]

Digital Library

[3]

M. Bauer. From Interaction Data to Plan Libraries: A Clustering Approach. In Proc. 16th Int. Joint Conf. on AI, pages 962-967, 1999.]]

Digital Library

[4]

A. Cypher, editor. Watch What I Do: Programming by Demonstration. MIT Press, Cambridge, MA, 1994.]]

Digital Library

[5]

A. Garland, N. Lesh, and C. Sidner. Learning Task Models for Collaborative Discourse. In Proc. of Workshop on Adaptation in Dialogue Systems, NAACL '01, pages 25-32, 2001.]]

[6]

Y. Gil and E. Melz. Explicit representations of problemsolving strategies to support knowledge acquisition. In Proc. 13th Nat. Conf. AI, pages 469-476, 1996.]]

[7]

B. Grosz and C. Sidner. Plans for discourse. In P. R. Cohen, J. Morgan, and M. E. Pollack, editors, Intentions in Communication, pages 417-444. MIT Press, Cambridge, MA, 1990.]]

[8]

J. Kim and Y. Gil. Acquiring problem-solving knowledge from end users: Putting interdependency models to the test. In Proc. 17th Nat. Conf. AI, pages 223-229, 2000.]]

Digital Library

[9]

T. Lau, P. Domingos, and D. S. Weld. Version space algebra and its application to programming by demonstration. In Proc. 17th Int. Conf. on Machine Learning, pages 527-534, 2000.]]

Digital Library

[10]

N. Lesh, C. Rich, and C. Sidner. Using Plan Recognition in Human-Computer Collaboration. In Proc. of the 7th Int. Conf. on User Modeling, pages 23-32, 1999.]]

Digital Library

[11]

N. Lesh, C. Rich, and C. Sidner. Collaborating with Focused and Unfocused Users under Imperfect Communication. In Proc. 9th Int. Conf. on User Modeling, pages 64-73, 2001.]]

Digital Library

[12]

H. Lieberman, editor. Your Wish is My Command: Programming by Example. Morgan Kaufmann, 2001.]]

Digital Library

[13]

K. E. Lochbaum. A Collaborative Planning Model of Intentional Structure. Computational Linguistics, 24(4):525-572, Dec. 1998.]]

Digital Library

[14]

T. Masui and K. Nakayama. Repeat and predict- two keys to efficient text editing. In Conference on Human Factors in Computing Systems, pages 118-123, 1994.]]

Digital Library

[15]

F. Paterno and C. Mancini. Developing task models from informal scenarios. In Proc. ACMSIGCHI '99, Late-Breaking Results, pages 228-229, 1999.]]

Digital Library

[16]

F. Paterno, G. Mori, and R. Galiberti. CTTE: An environment for analysis and development of task models of cooperative applications. In Proc. ACM SIGCHI '01, Extended Abstracts, pages 21-22, 2001.]]

Digital Library

[17]

C. Rich and C. Sidner. COLLAGEN: A Collaboration manager for Software Interface Agents. User Modeling and User- Adapted Interaction, 8(3/4):315-350, 1998.]]

Digital Library

[18]

C. Rich, C. Sidner, and N. Lesh. Collagen: Applying Collaborative Discourse Theory to Human-Computer Interaction. AI magazine, 22(4), 2001. To appear. http://www.merl.com/papers/TR2000-38/.]]

Digital Library

[19]

G. Tecuci, M. Boicu, K. Wright, S. W. Lee, D. Marcu, and M. Bowman. An integrated shell and methodology for rapid development of knowledge-based agents. In Proc. 16th Nat. Conf. AI, pages 250-257, 1999.]]

Digital Library

[20]

M. van Lent and J. Laird. Learning hierarchical performance knowledge by observation. In Proc. 16th Int. Conf. on Machine Learning, pages 229-238, 1999.]]

Digital Library

[21]

X. Wang. Learning by observation and practice: an incremental approach for planning operator acquisition. In Proc. 12th Int. Conf. on Machine Learning, pages 549-557, 1995.]]

Cited By

Cheng YSun LTomizuka M(2021)Human-Aware Robot Task Planning Based on a Hierarchical Task ModelIEEE Robotics and Automation Letters10.1109/LRA.2021.3056370(1-1)Online publication date: 2021
https://doi.org/10.1109/LRA.2021.3056370
Mohseni-Kabir ALi CWu VMiller DHylak BChernova SBerenson DSidner CRich C(2019)Simultaneous learning of hierarchy and primitives for complex robot tasksAutonomous Robots10.1007/s10514-018-9749-y43:4(859-874)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10514-018-9749-y
Hogg CMuñoz-Avila HKuter U(2016)Learning Hierarchical Task Models from Input TracesComputational Intelligence10.1111/coin.1204432:1(3-48)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1111/coin.12044
Show More Cited By

Index Terms

Learning hierarchical task models by defining and refining examples
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Learning settings
2. Software and its engineering
  1. Software creation and management
    1. Designing software
    2. Software development techniques

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Information & Contributors

Information

Published In

cover image ACM Conferences

K-CAP '01: Proceedings of the 1st international conference on Knowledge capture

October 2001

220 pages

ISBN:1581133804

DOI:10.1145/500737

Conference Chairs:
Yolanda Gil
University of Southern California/Information Sciences Institute
,
Mark Musen
Stanford University
,
Jude Shavlik
University of Wisconsin at Madison

Copyright © 2001 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 22 October 2001

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K-CAP01

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SIGAI

K-CAP01: International Conference on Knowledge Capture

October 22 - 23, 2001

British Columbia, Victoria, Canada

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K-CAP '01 Paper Acceptance Rate 26 of 82 submissions, 32%;

Overall Acceptance Rate 55 of 198 submissions, 28%

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Cited By

Cheng YSun LTomizuka M(2021)Human-Aware Robot Task Planning Based on a Hierarchical Task ModelIEEE Robotics and Automation Letters10.1109/LRA.2021.3056370(1-1)Online publication date: 2021
https://doi.org/10.1109/LRA.2021.3056370
Mohseni-Kabir ALi CWu VMiller DHylak BChernova SBerenson DSidner CRich C(2019)Simultaneous learning of hierarchy and primitives for complex robot tasksAutonomous Robots10.1007/s10514-018-9749-y43:4(859-874)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s10514-018-9749-y
Hogg CMuñoz-Avila HKuter U(2016)Learning Hierarchical Task Models from Input TracesComputational Intelligence10.1111/coin.1204432:1(3-48)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1111/coin.12044
Mohseni-Kabir ARich CChernova SSidner CMiller DAdams JSmart WMutlu BTakayama L(2015)Interactive Hierarchical Task Learning from a Single DemonstrationProceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction10.1145/2696454.2696474(205-212)Online publication date: 2-Mar-2015
https://dl.acm.org/doi/10.1145/2696454.2696474
Amershi SMahmud JNichols JLau TRuiz G(2013)LiveActionACM Transactions on Interactive Intelligent Systems10.1145/2533670.25336723:3(1-23)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1145/2533670.2533672
Blaylock Nde Beaumont WGalescu LJung HAllen JFerguson GSwift M(2012)Play-by-Play Learning for Textual User InterfacesApplied Natural Language Processing10.4018/978-1-60960-741-8.ch020(351-364)Online publication date: 2012
https://doi.org/10.4018/978-1-60960-741-8.ch020
Veeraraghavan HVaculin RVeloso M(2010)Learning Task Specific Web Services Compositions with Loops and Conditional Branches from Example ExecutionsProceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 0110.1109/WI-IAT.2010.292(581-588)Online publication date: 31-Aug-2010
https://dl.acm.org/doi/10.1109/WI-IAT.2010.292
(2010)ReferencesNo Code Required10.1016/B978-0-12-381541-5.00038-9(453-472)Online publication date: 2010
https://doi.org/10.1016/B978-0-12-381541-5.00038-9
Armentano MAmandi A(2009)Recognition of User Intentions for Interface Agents with Variable Order Markov ModelsProceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH10.1007/978-3-642-02247-0_18(173-184)Online publication date: 1-Sep-2009
https://dl.acm.org/doi/10.1007/978-3-642-02247-0_18
Yan Song Lei Huang (2008)The research of calculation model of QoS for service composition based on 0-1 programming2008 IEEE International Conference on Service Operations and Logistics, and Informatics10.1109/SOLI.2008.4682968(2562-2564)Online publication date: Oct-2008
https://doi.org/10.1109/SOLI.2008.4682968
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