Skip to main content
SpringerLink
Log in
Book cover

Foundations of Knowledge Acquisition pp 83–116Cite as

On Integrating Machine Learning with Planning

  • Chapter

Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 195))

Abstract

Domain-independent classical planning is faced with serious difficulties. Many of these are traceable to some facet of the frame problem. Perfect knowledge of the planner’s world and operators is impossible for most domains. With anything less, small unmodeled errors can result in large discrepancies between the expected and observed worlds. Such discrepancies may interfere with the achievement of a goal. Reactivity provides an extreme response. It allows only sensor information after an action is taken to judge the action’s effects, and abandons projection altogether. There must be a vast space of possibilities between the extremes of classical planning and reactivity. This paper describes two called computable planning and permissive planning. Machine learning, in the form of Explanation-Based Learning, is used in computable planning to recognize deferrable goals, resulting in many of the benefits offered by reactivity but in a domain independent form. In permissive planning, machine learning techniques are used to refine plans through experience so that they become less sensitive to the necessarily approximate knowledge.

The research reported in this paper was supported by the Office of Naval Research under grants N-00014-86-0309 and N-00014-91-J-1563.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Agre, P. & Chapman, D. (1987). Pengi: An Implementation of a Theory of Activity. Proceedings of the National Conference on Artificial Intelligence (pp. 268–272). Seattle, Washington: Morgan Kaufmann.

    Google Scholar 

  • Bennett, S. W. (1990). Reducing Real-world Failures of Approximate Explanation-based Rules. Proceedings of the Seventh International Conference on Machine Learning (pp. 226–234). Austin, Texas: Morgan Kaufmann.

    Google Scholar 

  • Brooks, R. A. (1982). Symbolic Error Analysis and Robot Planning (Memo 685). Cambridge: Massachusetts Institute of Technology, Artificial Intelligence Laboratory.

    Google Scholar 

  • Brooks, R. A. (1987). Planning is Just a Way of Avoiding Figuring Out What to Do Next (Working Paper 303). Cambridge: Massachusetts Institute of Technology, Artificial Intelligence Laboratory.

    Google Scholar 

  • Chapman, D. (1987). Planning for Conjunctive Goals. Artificial Intelligence 32,3, 333–378.

    Article  MATH  Google Scholar 

  • Chien, S. A. (1989). Using and Refining Simplifications: Explanation-based Learning of Plans in Intractable Domains. Proceedings of The Eleventh International Joint Conference on Artificial Intelligence (pp. 590–595). Detroit, Michigan: Morgan Kaufmann.

    Google Scholar 

  • Cohen, W. W. (1988). Generalizing Number and Learning from Multiple Examples in Explanation Based Learning. Proceedings of the Fifth International Conference on Machine Learning (pp. 256–269). Ann Arbor, Michigan: Morgan Kaufmann.

    Google Scholar 

  • Cohen, P. R., Greenberg, M. L., Hart, D. M., & Howe, A. E. (1989). Trial by Fire: Under-standing the Design Requirements for Agents in Complex Environments. Artificial Intelligence Magazine 10,5, 32–48.

    Google Scholar 

  • Davis, E. (1986). Representing and Acquiring Geographic Knowledge, Morgan Kaufmann.

    Google Scholar 

  • DeJong, G. F. & Mooney, R. J. (1986). Explanation-Based Learning: An Alternative View. Machine Learning 1,2, 145–176.

    Google Scholar 

  • Drummond, M. & Bresina, J. (1990). Anytime Synthetic Projection: Maximizing the Probability of Goal Satisfaction. Proceedings of the Eighth National Conference on Artificial Intelligence (138–144). Boston, Massachusetts: Morgan Kaufmann.

    Google Scholar 

  • Erdmann, M. (1986). Using Backprojections for Fine Motion Planning with Uncertainty. International Journal of Robotics Research 5,1, 19–45.

    Article  Google Scholar 

  • Fikes, R. E., Hart, P. E., & Nilsson, N. J. (1972). Learning and Executing Generalized Robot Plans. Artificial Intelligence 3,4, 251–288.

    Article  Google Scholar 

  • Firby, R. J. (1987). An Investigation into Reactive Planning in Complex Domains. Proceedings of the National Conference on Artificial Intelligence (202–206). Seattle, Washington: Morgan Kaufmann.

    Google Scholar 

  • Forbus, K. D. (1984). Qualitative Process Theory. Artificial Intelligence 24, 85–168.

    Article  Google Scholar 

  • Fox, B. R. & Kempf, K. G. (1985). Opportunistic Scheduling for Robotic Assembly. Proceedings of the Institute of Electrical and Electronics Engineers International Conference on Robotics and Automation (880–889).

    Google Scholar 

  • Gervasio, M. T. (1990a). Learning Computable Reactive Plans Through Achievability Proofs (Technical Report UIUCDCS-R-90-1605). Urbana: University of Illinois, Department of Computer Science.

    Google Scholar 

  • Gervasio, M. T. (1990b). Learning General Completable Reactive Plans. Proceedings of the Eighth National Conference on Artificial Intelligence (1016–1021). Boston, Massachusetts: Morgan Kaufmann.

    Google Scholar 

  • Gervasio, M. T. & DeJong, G. F. (1991). Learning Probably Completable Plans (Technical Report UIUCDCS-91-1686). Urbana: University of Illinois, Department of Computer Science.

    Google Scholar 

  • Hammond, K. (1986). Learning to Anticipate and Avoid Planning Failures through the Explanation of Failures. Proceedings of the National Conference on Artificial Intelligence (pp. 556–560). Philadelphia, Pennsylvania: Morgan Kaufmann.

    Google Scholar 

  • Hammond, K., Converse, T., & Marks, M. (1990). Towards a Theory of Agency. Proceedings of the Workshop on Innovative Approaches to Planning Scheduling and Control (pp. 354–365). San Diego, California: Morgan Kaufmann.

    Google Scholar 

  • Homem de Mello, L. S. & Sanderson, A. C. (1986). And/Or Graph Representation of Assembly Plans. Proceedings of the National Conference on Artificial Intelligence (pp. 1113–1119). Philadelphia, Pennsylvania: Morgan Kaufmann.

    Google Scholar 

  • Hutchinson, S. A. & Kak, A. C. (1990). Span A Planner That Satisfies Operational and Geometric Goals in Uncertain Environments. Artificial Intelligence Magazine 11,1, 30–61.

    Google Scholar 

  • Kaelbling, L. P. (1986). An Architecture for Intelligent Reactive Systems. Proceedings of the 1986 Workshop on Reasoning About Actions & Plans (pp. 395–410). Timberline, Oregon: Morgan Kaufmann.

    Google Scholar 

  • Lozano-Perez, T., Mason, M. T., & Taylor, R. H. (1984). Automatic Synthesis of Fine-Motion Strategies for Robots. International Journal of Robotics Research 3,1, 3–24.

    Article  Google Scholar 

  • Malkin, P. K. & Addanki, S. (1990). LOGnets: A Hybrid Graph Spatial Representation for Robot Navigation. Proceedings of the Eighth National Conference on Artificial Intelligence (1045–1050). Boston, Massachusetts: Morgan Kaufmann.

    Google Scholar 

  • Martin, N. G. & Allen, J. F. (1990). Combining Reactive and Strategic Planning through Decomposition Abstraction. Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling and Control (pp. 137–143). San Diego, California: Morgan Kaufmann.

    Google Scholar 

  • Minton, S. (1988). Learning Search Control Knowledge: An Explanation-Based Approach. Norwell: Kluwer Academic Publishers.

    Google Scholar 

  • Mitchell, T. M., Mahadevan, S. & Steinberg, L. I. (1986). LEAP: A Learning Apprentice for VLSI Design. Proceedings of the Ninth International Joint Conference on Artificial Intelligence (pp. 573–580). Los Angeles, California: Morgan Kaufmann.

    Google Scholar 

  • Mitchell, T. M., Keller, R., & Kedar-Cabelli, S. (1986). Explanation-Based Generalization: A Unifying View. Machine Learning 1,1, 47–80.

    Google Scholar 

  • Mitchell, T. M. (1990). Becoming Increasingly Reactive. Proceedings of the Eighth National Conference on Artificial Intelligence (1051–1058). Boston, Massachusetts: Morgan Kaufmann.

    Google Scholar 

  • Mooney, R. J. and Bennett, S. W. (1986). A Domain Independent Explanation-Based Generalizer. Proceedings of the National Conference on Artificial Intelligence (pp. 551–555). Philadelphia, Pennsylvania: Morgan Kaufmann.

    Google Scholar 

  • Mooney, R. J. (1990). A General Explanation-Based Learning Mechanism and its Application to Narrative Understanding. London: Pitman.

    Google Scholar 

  • Mostow, J. & Bhatnagar, N. (1987). FAILSAFE—A Floor Planner that uses EBG to Learn from its Failures. Proceedings of the Tenth International Joint Conference on Artificial Intelligence. Milan, Italy: Morgan Kaufmann.

    Google Scholar 

  • Rosenschein, S. J. & Kaelbling, L. P. (1987). The Synthesis of Digital Machines with Provable Epistemic Properties (CSLI-87-83). Stanford: CSLI.

    Google Scholar 

  • Sacerdoti, E. (1974). Planning in a Hierarchy of Abstraction Spaces. Artificial Intelligence 5, 115–135.

    Article  MATH  Google Scholar 

  • Schoppers, M. J. (1987). Universal Plans for Reactive Robots in Unpredictable Environments. Proceedings of the Tenth International Joint Conference on Artificial Intelligence (pp. 1039–1046). Milan, Italy: Morgan Kaufmann.

    Google Scholar 

  • Segre, A. M. (1988). Machine Learning of Robot Assembly Plans. Norwell: Kluwer Academic Publishers.

    Google Scholar 

  • Shavlik, J. W. & DeJong, G. F. (1987). An Explanation-Based Approach to Generalizing Number. Proceedings of the Tenth International Joint Conference on Artificial Intelligence (pp. 236–238). Milan, Italy: Morgan Kaufmann.

    Google Scholar 

  • Shell, P. & Carbonell, J. (1989). Towards a General Framework for Composing Disjunctive and Iterative Macro-operators. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 596–602). Detroit, Michigan: Morgan Kaufmann.

    Google Scholar 

  • Stefik, M. (1981). Planning and Metaplanning (MOLGEN: Part 2). Artificial Intelligence 16,2, 141–170.

    Article  Google Scholar 

  • Suchman, L. A. (1987). Plans and Situated Actions, Cambridge: Cambridge University Press.

    Google Scholar 

  • Tadepalli, P. (1989). Lazy Explanation-Based Learning: A Solution to the Intractable Theory Problem. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Detroit, Michigan: Morgan-Kaufmann.

    Google Scholar 

  • Turney, J. & Segre, A. (1989). SEPIA: An Experiment in Integrated Planning and Improvisation. Proceedings of The American Association for Artificial Intelligence Spring Symposium on Planning and Search (pp. 59–63).

    Google Scholar 

  • Whitehall, B. L. (1987). Substructure Discovery in Executed Action Sequences (Technical Report UILU-ENG-87-2256). Urbana: University of Illinois, Department of Computer Science.

    Google Scholar 

  • Wilkins, D. E. (1988). Practical Planning: Extending the Classical Artificial Intelligence Planning Paradigm. San Mateo: Morgan Kaufman.

    Google Scholar 

  • Wong, E. K. & and Fu, K. S. (1985). A Hierarchical Orthogonal Space Approach to Collision-Free Path Planning. Proceedings of the 1985 Institute of Electrical and Electronics Engineers International Conference on Robotics and Automation (506–511).

    Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy Sets. Information and Control 8,3, 338–353.

    Article  MATH  Google Scholar 

  • Zhu, D. J. and Latombe, J. C. (1990). Constraint Reformulation in a Hierarchical Path Planner. Proceedings of the 1990 Institute of Electrical and Electronics Engineers International Conference of Robotics and Automation (pp. 1918–1923). Cincinnati, Ohio: Morgan Kaufmann.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Beckman Institute & Computer Science Department, University of Illinois, 405 N. Mathews Ave., Urbana, IL, 61801

    Gerald F. DeJong, Melinda T. Gervasio & Scott W. Bennett

Authors
  1. Gerald F. DeJong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Melinda T. Gervasio
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Scott W. Bennett
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Naval Research Laboratory, USA

    Alan L. Meyrowitz

  2. Office of Naval Research, USA

    Susan Chipman

Rights and permissions

Reprints and permissions

Copyright information

© 1993 Kluwer Academic Publishers

About this chapter

Cite this chapter

DeJong, G.F., Gervasio, M.T., Bennett, S.W. (1993). On Integrating Machine Learning with Planning. In: Meyrowitz, A.L., Chipman, S. (eds) Foundations of Knowledge Acquisition. The Springer International Series in Engineering and Computer Science, vol 195. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-27366-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-0-585-27366-2_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-9278-1

  • Online ISBN: 978-0-585-27366-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation