LHTNDT: LEARN HTN METHOD PRECONDITIONS USING DECISION TREE

Fatemeh Nargesian, Gholamreza Ghassem-Sani

2008

Abstract

In this paper, we describe LHTNDT, an algorithm that learns the preconditions of HTN methods by examining plan traces produced by another planner. LHTNDT extracts conditions for applying methods by using decision tree based algorithm. It considers the state of relevant domain objects in both current and goal states. Redundant training samples are removed using graph isomorphism. Our experiments, LHTNDT converged. It can learn most of preconditions correctly and quickly. 80% of our test problems were solved by preconditions extracted by ¾ of plan traces needed for full convergence.

References

  1. Aha, D. W.; and Breslow, L. A. 1997. Refining Conversational Case Libraries. In Proceedings of the Second International Conference on Case-Based Reasoning, pp. 267-278. Providence, RI: Springer Press/Verlag Press.
  2. Bergmann, R.; and Wilke, W. 1995. Building and Refining Abstract Planning Cases by Change of Representation Language. Journal of Artificial Intelligence Research, pp. 53-118.
  3. Botea, A.; Muller, M.; and Schaeffer, J. 2005. Learning Partial-order Macros from Solutions. In Proceedings of the 15th International Conference on Automated Planning and Scheduling (ICAPS-05). AAAI Press.
  4. Choi, D.; and Langley, P. 2005. Learning Teleoreactive Logic Programs from Problem Solving. In Proceedings of the 15th International Conference on Inductive Logic Programming. Springer.
  5. Erol, K.; Hendler, J.; and Nau, D. S. 1996. Complexity Results for Hierarchical Task-Network Planning. Annual of Mathematics and Artificial Intelligence 18(1), pp. 69-93.
  6. Fikes, R. E.; and Nilsson, N. J. 1971. STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. Artificial Intelligence (2), pp 189- 208.
  7. Gupta, N.; and Nau, D. 1992. On the complexity of Blocks-world Planning. Artificial Intelligence 56(2-3): pp. 223-254.
  8. Hogg, Ch. 2007. From Task Definitions and Plan Traces to HTN Methods, Workshop of International Conference on Automated Planning and Scheduling (ICAPS-07).
  9. Ilghami, O.; Nau, D. S.; Munoz-Avila, H.; and Aha, D. 2005. Learning Preconditions for Planning from Plan Traces and HTN Structure.
  10. Ilghami, O.; Munoz-Avila, H.; Nau, D. S.; and Aha. D. 2005. Learning Approximate Preconditions for Methods in Hierarchical Plans. In Proceedingsof the 22nd International Conference on Machine Learning, Bonn, Germany.
  11. Ilghami, O.; Nau, D. S.; and Munoz-Avila, H. 2006. Learning to Do HTN Planning, International Conference on Automated Planning and Scheduling (ICAPS-06).
  12. Knoblock, C. 1993. Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning. Norwell, MA: Kluwer Academic Publishers.
  13. Korf, R. E. “Planning as Search: A Quantitative Approach”, Artificial Intelligence, 33(1), pp. 65-88, 1987.
  14. Mooney, R. J. 1998. Generalizing the Order of Operators in Macro-Operators. Machine Learning, pp. 270-283.
  15. Munoz-Avila, H.; McFarlane, D.; Aha, D. W.; Ballas, J.; Breslow, L. A.; and Nau, D. S. 1999. Using Guidelines to Constrain Case-based HTN Planning. In Proceedings of the Third International Conference on Case-Based Reasoning, 288-302. Providence, RI: Springer Press.
  16. Nau, D. S.; Cao, Y.; Lotem, A.; and Munoz-Avila, H. 1999. SHOP: Simple Hierarchical Ordered Planner. In Proceedings of the Sixteenth International Joint conference on Artificial Intelligence, 968-973. Stockholm: AAAI Press.
  17. Reddy, C.; and Tadepalli, P. 1997. Learning Goaldecomposition Rules using Exercises. In Proceedings of the International Conference on Machine Learning (ICML-97).
  18. Ruby, D.; and Kibler, D. F. 1991. Steppingstone: An Empirical and Analytic Evaluation. In Proceedings of the 9th National Conference on artificial Intelligence: pp. 527-531.
  19. Sacerdoti, E. 1975. The Nonlinear Nature of Plans. In Proceedings of the 4th International Joint Conference on Artificial Intelligence, Tiblisi, USSR, pp. 206-214.
  20. Slaney J. K.; and Thiébaux S. 2001. Blocks World revisited. Artificial Intelligence 125(1-2): pp. 119-153.
  21. Tate, A.; 1977. Generating Project Networks. In Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, pp. 888- 893. MIT Press.
  22. Xu, K.; and Munoz-Avila, H. 2005. A Domainindependent System for Case-based Task Decomposition without Domain Theories. In Proceedingsof the 20th National Conference on Artificial Intelligence (AAAI-05). AAA Press.
  23. Zimmerman, T.; Kambhampati, S. 2003. Learningassisted Automated Planning: Looking back, tracking Stock, Going Forward. AI Magazine, 73-96.
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Paper Citation


in Harvard Style

Nargesian F. and Ghassem-Sani G. (2008). LHTNDT: LEARN HTN METHOD PRECONDITIONS USING DECISION TREE . In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8111-30-2, pages 60-65. DOI: 10.5220/0001499900600065


in Bibtex Style

@conference{icinco08,
author={Fatemeh Nargesian and Gholamreza Ghassem-Sani},
title={LHTNDT: LEARN HTN METHOD PRECONDITIONS USING DECISION TREE},
booktitle={Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2008},
pages={60-65},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001499900600065},
isbn={978-989-8111-30-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - LHTNDT: LEARN HTN METHOD PRECONDITIONS USING DECISION TREE
SN - 978-989-8111-30-2
AU - Nargesian F.
AU - Ghassem-Sani G.
PY - 2008
SP - 60
EP - 65
DO - 10.5220/0001499900600065