Advertisement

On Integrating Machine Learning with Planning

  • Gerald F. DeJong
  • Melinda T. Gervasio
  • Scott W. Bennett
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (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.

Keywords

Plan Component Failure Recovery Planning Concept Repeat Loop Classical Planning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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
  2. 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
  3. Brooks, R. A. (1982). Symbolic Error Analysis and Robot Planning (Memo 685). Cambridge: Massachusetts Institute of Technology, Artificial Intelligence Laboratory.Google Scholar
  4. 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
  5. Chapman, D. (1987). Planning for Conjunctive Goals. Artificial Intelligence 32,3, 333–378.MATHCrossRefGoogle Scholar
  6. 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
  7. 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
  8. 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
  9. Davis, E. (1986). Representing and Acquiring Geographic Knowledge, Morgan Kaufmann.Google Scholar
  10. DeJong, G. F. & Mooney, R. J. (1986). Explanation-Based Learning: An Alternative View. Machine Learning 1,2, 145–176.Google Scholar
  11. 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
  12. Erdmann, M. (1986). Using Backprojections for Fine Motion Planning with Uncertainty. International Journal of Robotics Research 5,1, 19–45.CrossRefGoogle Scholar
  13. Fikes, R. E., Hart, P. E., & Nilsson, N. J. (1972). Learning and Executing Generalized Robot Plans. Artificial Intelligence 3,4, 251–288.CrossRefGoogle Scholar
  14. 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
  15. Forbus, K. D. (1984). Qualitative Process Theory. Artificial Intelligence 24, 85–168.CrossRefGoogle Scholar
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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.CrossRefGoogle Scholar
  26. 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
  27. 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
  28. Minton, S. (1988). Learning Search Control Knowledge: An Explanation-Based Approach. Norwell: Kluwer Academic Publishers.Google Scholar
  29. 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
  30. Mitchell, T. M., Keller, R., & Kedar-Cabelli, S. (1986). Explanation-Based Generalization: A Unifying View. Machine Learning 1,1, 47–80.Google Scholar
  31. Mitchell, T. M. (1990). Becoming Increasingly Reactive. Proceedings of the Eighth National Conference on Artificial Intelligence (1051–1058). Boston, Massachusetts: Morgan Kaufmann.Google Scholar
  32. 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
  33. Mooney, R. J. (1990). A General Explanation-Based Learning Mechanism and its Application to Narrative Understanding. London: Pitman.Google Scholar
  34. 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
  35. Rosenschein, S. J. & Kaelbling, L. P. (1987). The Synthesis of Digital Machines with Provable Epistemic Properties (CSLI-87-83). Stanford: CSLI.Google Scholar
  36. Sacerdoti, E. (1974). Planning in a Hierarchy of Abstraction Spaces. Artificial Intelligence 5, 115–135.MATHCrossRefGoogle Scholar
  37. 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
  38. Segre, A. M. (1988). Machine Learning of Robot Assembly Plans. Norwell: Kluwer Academic Publishers.Google Scholar
  39. 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
  40. 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
  41. Stefik, M. (1981). Planning and Metaplanning (MOLGEN: Part 2). Artificial Intelligence 16,2, 141–170.CrossRefGoogle Scholar
  42. Suchman, L. A. (1987). Plans and Situated Actions, Cambridge: Cambridge University Press.Google Scholar
  43. 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
  44. 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
  45. 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
  46. Wilkins, D. E. (1988). Practical Planning: Extending the Classical Artificial Intelligence Planning Paradigm. San Mateo: Morgan Kaufman.Google Scholar
  47. 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
  48. Zadeh, L. A. (1965). Fuzzy Sets. Information and Control 8,3, 338–353.MATHCrossRefGoogle Scholar
  49. 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

Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • Gerald F. DeJong
    • 1
  • Melinda T. Gervasio
    • 1
  • Scott W. Bennett
    • 1
  1. 1.Beckman Institute & Computer Science DepartmentUniversity of IllinoisUrbana

Personalised recommendations