skip to main content
10.1145/1102351.1102394acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicmlConference Proceedingsconference-collections
Article

Learning approximate preconditions for methods in hierarchical plans

Published: 07 August 2005 Publication History

Abstract

A significant challenge in developing planning systems for practical applications is the difficulty of acquiring the domain knowledge needed by such systems. One method for acquiring this knowledge is to learn it from plan traces, but this method typically requires a huge number of plan traces to converge. In this paper, we show that the problem with slow convergence can be circumvented by having the learner generate solution plans even before the planning domain is completely learned. Our empirical results show that these improvements reduce the size of the training set that is needed to find correct answers to a large percentage of planning problems in the test set.

References

[1]
Erol, K., Hendler, J., & Nau, D. S. (1996). Complexity results for hierarchical task-network planning. Annals of Mathematics and Artificial Intelligence, 18, 69--93.
[2]
Garland, A., Ryall, K., & Rich, C. (2001). Learning hierarchical task models by defining and refining examples. Proceedings of the 1st Int'l Conference on Knowledge Capture (pp. 44--51).
[3]
Hirsh, H., Mishra, N., & Pitt, L. (1997). Version spaces without boundary sets. Proceedings of the 14th Nat'l Conference on Artificial Intelligence (pp. 491--496).
[4]
Hirsh, H., Mishra, N., & Pitt, L. (2004). Version spaces and the consistency problem. Artificial Intelligence, 156, 115--138.
[5]
Huang, Y.-C., Selman, B., & Kautz, H. A. (2000). Learning declarative control rules for constraint-based planning. Proceedings of the 17th Int'l Conference on Machine Learning (pp. 415--422).
[6]
Ilghami, O., Nau, D., Muñoz-Avila, H., & Aha, D. (2002). CaMeL: Learning method preconditions for HTN planning. Proceedings of the 6th Int'l Conference on AI Planning and Scheduling (pp. 168--178).
[7]
Khardon, R. (1999). Learning action strategies for planning domains. Artificial Intelligence, 113, 125--148.
[8]
Langley, P., & Rogers, S. (2004). Cumulative learning of hierarchical skills. Proceedings of the Third International Conference on Development and Learning.
[9]
Martin, M., & Geffner, H. (2000). Learning generalized policies in planning using concept languages. Proceedings of the 7th Int'l Conference on Knowledge Representation and Reasoning (pp. 667--677).
[10]
Mitchell, T. M. (1977). Version spaces; A candidate elimination approach to rule learning. Proceedings of the 5th Int'l Joint Conference on Artificial Intelligence (pp. 305--310).
[11]
Nau, D. S., Cao, Y., Lotem, A., & Muññoz-Avila, H. (1999). SHOP: Simple hierarchical ordered planner. Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (pp. 968--973).
[12]
Reddy, C., & Tadepalli, P. (1997). Learning goal-decomposition rules using exercises. 14th Int'l Conference on Machine Learning (pp. 278--286).
[13]
Ruby, D., & Kibler, D. (1993). Learning recurring subplans. In S. Minton (Ed.), Machine learning methods for planning, 467--497. San Mateo, CA: Kaufmann.
[14]
Sacerdoti, E. (1975). The nonlinear nature of plans. Proceedings of the Fourth International Joint Conference on Artificial Intelligence (pp. 206--214).
[15]
Tate, A. (1977). Generating project networks. Proceedings of the Fifth International Joint Conference on Artificial Intelligence (pp. 888--893).
[16]
van Lent, M., & Laird, J. (1999). Learning hierarchical performance knowledge by observation. Proceedings of the Sixteenth International Conference on Machine Learning (pp. 229--238).

Cited By

View all
  • (2023)Towards Dynamic Action Planning with user preferences in Automated Health CoachingSmart Health10.1016/j.smhl.2023.10038928(100389)Online publication date: Jun-2023
  • (2022)Recent Advances in Artificial Intelligence for Wireless Internet of Things and Cyber–Physical Systems: A Comprehensive SurveyIEEE Internet of Things Journal10.1109/JIOT.2022.31704499:15(12916-12930)Online publication date: 1-Aug-2022
  • (2017)Automated learning of hierarchical task networks for controlling minecraft agents2017 IEEE Conference on Computational Intelligence and Games (CIG)10.1109/CIG.2017.8080440(226-231)Online publication date: Aug-2017
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICML '05: Proceedings of the 22nd international conference on Machine learning
August 2005
1113 pages
ISBN:1595931805
DOI:10.1145/1102351
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2005

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Article

Acceptance Rates

Overall Acceptance Rate 140 of 548 submissions, 26%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Towards Dynamic Action Planning with user preferences in Automated Health CoachingSmart Health10.1016/j.smhl.2023.10038928(100389)Online publication date: Jun-2023
  • (2022)Recent Advances in Artificial Intelligence for Wireless Internet of Things and Cyber–Physical Systems: A Comprehensive SurveyIEEE Internet of Things Journal10.1109/JIOT.2022.31704499:15(12916-12930)Online publication date: 1-Aug-2022
  • (2017)Automated learning of hierarchical task networks for controlling minecraft agents2017 IEEE Conference on Computational Intelligence and Games (CIG)10.1109/CIG.2017.8080440(226-231)Online publication date: Aug-2017
  • (2017)An algorithm for online planning to improve availability and performance of self-adaptive websites2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)10.1109/CFIS.2017.8003685(213-218)Online publication date: Mar-2017
  • (2017)HTN guided game tree search for adaptive CGF commander behavior modeling2017 IEEE International Conference on Agents (ICA)10.1109/AGENTS.2017.8015306(78-83)Online publication date: Jul-2017
  • (2016)Learning Hierarchical Task Models from Input TracesComputational Intelligence10.1111/coin.1204432:1(3-48)Online publication date: 1-Feb-2016
  • (2014)A developmentally inspired transfer learning approach for predicting skill durations4th International Conference on Development and Learning and on Epigenetic Robotics10.1109/DEVLRN.2014.6982979(181-186)Online publication date: Oct-2014
  • (2012)Review: a review of machine learning for automated planningThe Knowledge Engineering Review10.1017/S026988891200001X27:4(433-467)Online publication date: 1-Nov-2012
  • (2010)Inductive Generalization of Analytically Learned Goal HierarchiesInductive Logic Programming10.1007/978-3-642-13840-9_7(65-72)Online publication date: 2010
  • (2009)Inductive generalization of analytically learned goal hierarchiesProceedings of the 19th international conference on Inductive logic programming10.5555/1893538.1893545(65-72)Online publication date: 2-Jul-2009
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media