Skip to main content
SpringerLink
Log in

Inductive program synthesis for therapy plan generation

  • Special Issue
  • Published:
New Generation Computing Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Planning is investigated in an area where classical STRIPS-like approaches usually fail. The application domain is therapy (i.e. repair) for complex dynamic processes. The peculiarities of this domain are discussed in some detail for convincingly developing the characteristics of the inductive planning approach presented. Plans are intended to be run for process therapy. Thus, plans are programs. Because of the unavoidable vagueness and uncertainty of information about complex dynamic processes in the case of disturbance, therapy plan generation turns out to be inductive program synthesis. There is developed a graph-theoretically based approach to inductive therapy plan generation. This approach is investigated from the inductive inference perspective. Particular emphasis is put on consistent and incremental learning of therapy plans. Basic application scenarios are developed and compared to each other. The inductive inference approach is invoked to develop and investigate a couple of planning algorithms. The core versions of these algorithms are successfully implemented in Lisp and Prolog.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Arnold, O. and Jantke, K. P., “Therapy Plan Generation as Program Synthesis,” inAlgorithmic Learning Theory, Proc. 4th International Workshop on Analogical and Inductive Inference (AII’94) and the 5th International Workshop on Algorithmic Learning Theory (ALT’94) (Setsuo Arikawa and Klaus P. Jantke, eds.), October 10–15, 1994, Reinhardsbrunn Castle, Germany,volume 872 of LNAI, Springer-Verlag, pp. 40–55, 1994.

  2. Arnold, O. and Jantke, K. P., “Therapy Plan Generation in Complex Dynamic Environments,”ICSI Report, TR-94-054, International Computer Science Institute, Berkeley, California, October 1994.

    Google Scholar 

  3. Arnold, O. and Jantke, K. P., “Therapy Plans as Hierarchically Structured Graphs,” inFifth International Workshop on Graph Grammars and their Application to Computer Science, Williamsburg, Virginia, USA, November 1994.

  4. Arnold O. and Jantke, K. P., “Therapy Plans as Hierarchically Structured Graphs,”WISCON Report 02/94, HTWK Leipzig (FH), FB Informatik, Mathematik & Naturwissenschaften, April 1994.

  5. Arnold, O. and Jantke, K. P., “Anwendung einer Logik der Constraints in der operativen Prozeßführung,” inLogik in der Informatik, 3. Jahrestagung der GI-Fachgruppe 0.1.6 (Peter H. Schmitt, ed.), Universität Karlsruhe, Fakultät für Informatik, Bericht 23/95, pp. 13–27, Juni 1995.

  6. Angluin, D. “Computational Learning Theory: Survey and Selected Bibliography,” inACM Symposium on Theory of Computing, STOC’92, ACM Press, pp. 351–368, 1992.

  7. Arnold, O. “Wissensverarbeitung in dynamischen Prozeßumgebungen: Reaktive Therapieplanung in dynamischen Prozeßumgebungen,”WISCON Report, 10/92, Technische Hochschule Leipzig, FB Mathematik & Informatik, October 1992.

  8. Arnold, O. “Entwicklungswerkzeug zur Analyse generierter Pläne “Plan-Tracer,”,”WISCON Report, 03/93, HTWK Leipzig (FH), Fachbereich IMN, December 1993.

  9. Arnold, O. “A Logic of Constraints for Dynamic Process Control,”WISCON Report, 09/94, HTWK Leipzig (FH), Fachbereich IMN, December 1994.

  10. Arnold, O. “WISCON — Ein System zur wissensbasierten Überwachung und Steuerung technischer Prozesse,”,Sonderheft “Informatiktage Leipzig 1994”, HTWK — Beiträge zu Lehre und Forschung, Hochschulzeitschrift, 2, 1995.

  11. Angluin, D. and Smith, C. H., “A Survey of Inductive Inference: Theory and Methods,”Computing Surveys, 15, pp. 237–269, 1983.

    Article  MathSciNet  Google Scholar 

  12. Barstow, D. R., “An Experiment in Knowledge-Based Automatic Programming,”Artificial Intelligence, 12, 1, pp. 73–119, 1979.

    Article  Google Scholar 

  13. Bauer, M. A., “Programming by Examples,”Artificial Intelligence, 12, 1, pp. 1–21, 1979.

    Article  MATH  Google Scholar 

  14. Bibel, W. and Biermann, A. W., “Special Issue: Automatic Programming-Foreword of the Guest Editors,”Journal of Symbolic Computation, 15, 5 & 6, pp. 463–465, 1993.

    Article  Google Scholar 

  15. Biermann, A. W., Baum, R. I., Petry, F. E., “Speeding up the Synthesis of Programs from Traces,”IEEE Transactions on Computers, 24, 2, pp. 122–136, 1975.

    Article  MATH  Google Scholar 

  16. Biundo, S., Günter, A., Hertzberg, J., Schneeberger, J., and Tank, W. “Planen und Konfigurieren,” inEinführung in die künstliche Intelligenz (Günther Görz, ed.), Addison-Wesley, pp. 767-828, 1993.

  17. Biermann, A. W. and Krishnaswamy, R., “Constructing Programs from Example Computations,”IEEE Transactions on Software Engineering, SE-2, 3, pp. 141–153, 1976.

    Article  MathSciNet  Google Scholar 

  18. Brazma, A. and Kinber, E. B., “Generalized Regular Expressions — A Language for Synthesis of Programs with Branching in Loops,”Theoretical Computer Science, 46, pp. 175–195, 1986.

    Article  MATH  MathSciNet  Google Scholar 

  19. Balzer, D., Kirbach, V., and May, V., “Knowledge Based Process Control,” inIntegration of Design, Implementation and Application on Measurement, Automation and Control, Intercama Congress 92 (Hartwig Steusloff and Martin Polke, eds.), München, Oldenbourg Verlag, pp. 33–49, 1992.

    Google Scholar 

  20. Bhatnagar, R., Miller, D. W., Hajek, B. K., and Chandasekaran, B., “DPRL: A language for Representation of Operation and Safety Maintenanc Procedures of Nuclear Power Plants,” inProceedings of the Third International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-90), Charleston, SC, July 15–18, 1990, New York, NY, pp. 593–600, 1990.

  21. Chapman, D., “Planning for Conjunctive Goals,”Artificial Intelligence, 32, pp. 333–377, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  22. Dershowitz, N. and Pinchover, E., “Inductive Synthesis of Equational Programs,” inAAAI-90, Proceedings, Eighth National Conference on Artificial Intelligence, MIT Press, pp. 234–239, 1990.

  23. Dershowitz, N. and Reddy, U. S., “Deductive and Inductive Synthesis of Equational Programs,”Journal of Symbolic Computation, 15, 5 & 6, pp. 467–494, 1993.

    Article  MATH  MathSciNet  Google Scholar 

  24. Dean, T. L. and Wellman, M. P.Planning and Control, Morgan Kaufmann, 1991.

  25. Flener, P. and Deville, Y., “Logic Program Synthesis from Incomplete Specifications,”Journal of Symbolic Computation, 15, 5 & 6, pp. 775–805, 1993.

    Article  MATH  MathSciNet  Google Scholar 

  26. Friedrich, G., “Model-Based Diagnosis and Repair,”AICOM, 6, 3/4, pp. 187–206, 1993.

    Google Scholar 

  27. Gabbay, D. M., Hodkinson, I., and Reynolds, M., “Temporal Logic. Mathematical Foundations and Computational Aspects,”Oxford Logic Guides, Vol. 28, Clarendon Press, Oxford, 1994.

    Google Scholar 

  28. Ginsberg, M.,Essentials of Artificial Intelligence, Morgan Kaufmann, 1993.

  29. Hammond, K.,Case Based Planning, Viewing Planning as a Memory Task, Academic Press, 1989.

  30. Hertzberg, J.,Planen. Einführung in die Planerstellungsmethoden der Künstlichen Intelligenz, BI Wissenschaftsverlag, 1989.

  31. Hertzberg, J., “KI-Handlungsplanung — Woran wir arbeiten, und woran wir arbeiten sollten,” inGrundlagen und Anwendungen der Künstlichen Intelligenz, 17, Fachtagung für Künstliche Intelligenz (KI’93) (Otthein Herzog, Thomas Christaller, and Dieter Schütt, eds.), Springer-Verlag, pp. 3–27, 1993.

  32. Höfting, F., Lengauer, T., and Wanke, E., “Processing of Hierarchically Defined Graphs and Graph Famillies,” inData Structures and Efficient Algorithms, volume 594 of Lecture Notes in Computer Science (Burkhard Monien and Thomas Ottmann, eds.), Springer-Verlag, pp. 44–69, 1992.

  33. Jantke, K. P. and Arnold, O., “Graphgrammatik-Konzepte in der Therapieplanung für komplexe dynamische Prozesse,” in4. GI Theorietag “Automaten und Formale Sprachen” (Markus Holzer, ed.),1994, Proc., Herrsching, Universität Tübingen, Wilhelm-Schickard-Institut, pp. 42–47, 1995.

  34. Jantke, K. P., and Arnold, O., “A Modal Temporal Logic and Its Models Underlying Variants of Planning Algorithms,” inTIME-96, Intern. Workshop, Key West, FL, USA, May 19–20, 1996, 1996.

  35. Jantke, K. P., Arnold, O., and Lehmann, T., “Flexible Simulation Scenarios for Real-Time Planning in Dynamic Environments,” inProc. Florida AI Research Symposium (FLAIRS-96), Special Track on Real-Time Planning and Reacting, Key West, FL, USA, May 20–22, 1996, 1996.

  36. Jantke, K. P. “Algorithmic Learning from Incomplete Information: Principles and Problems,” inMachines, Languages, and Complexity (Jürgen Dassow and Jozef Kelemen, eds.),volume 381 of Lecture Notes in Computer Science, Springer-Verlag, pp. 188–207, 1989.

  37. Jantke, K. P. “Types of Incremental Learning,” inWorking Notes, AAAI Symposium on Training Issues in Incremental Learning, March 23–25, 1993, Stanford, CA, USA, Stanford University, pp. 26–32, 1993.

  38. Jantke, K. P. and Beick, H.-R., Combining Postulates of Naturalness in Inductive Inferenc,EIK, 17, 8/9, pp. 465–484, 1981.

    MATH  MathSciNet  Google Scholar 

  39. Jouannaud, J.-P. and Kodratoff, Y., “Program Synthesis from Examples of Behavior,” inComputer Program Synthesis Methodologies (Alan W. Biermann and Gérard Guiho, eds.), D. Reided Publ. Co., pp. 213–250, 1983.

  40. Jantke, K. P., Lehmann, T., and Arnold, O., “Meeting Real-Time Constraints in Therapy Plan Generation for Complex Dynamic Systems by Flexible Simulation Scenarios,” inProc. ECHT ZEIT’96, Karlsruhe, June 18–20, 1996, Franzis-Verlag, 1996.

  41. Jantke, K. P. and Matuschek, D., “Developing and Implementing Planning Heuristics in Prolog,” inProc. Florida AI Research Symposium (FLAIRS-96), Key West, FL, USA, May 20–22, 1996, 1996.

  42. Kirsten, D., “Properties of Formal Languages of Therapy Plans Created by Graph Grammars,”Communications of the Algorithmic Learning Group CALG-04/95, Hochschule für Technik, Wirtschaft und Kultur Leipzig, FB Informatik, Mathematik und Naturwissenschaften, September 1995.

  43. Kirsten, D., “Redundant Edges in Therapy Planning,”Communications of the Algorithmic Learning Group CALG-06/95, Hochschule für Technik, Wirtschaft und Kultur Leipzig, FB Informatik, Mathematik und Naturwissenschaften, December 1995.

  44. Klette, R. and Wiehagen, R., “Research in the Theory of Inductive Inference by GDR Mathematicians — A Survey,”Information Sciences, 22, pp. 149–160, 1980.

    Article  MATH  MathSciNet  Google Scholar 

  45. Lengauer, T. and Wanke, E., “Efficient Solution of Connectivity Problems on Hierarchically Defined Graphs,”SIAM Journal of Computing, 17, 6, pp. 1063–1080, 1988.

    Article  MATH  MathSciNet  Google Scholar 

  46. Matuschek, D., Jantke, K. P., and Arnold, O., “Generierung von Therapieplänen mit Mitteln der logischen Programmierung,” inLogik in der Informatik, 3. Jahrestagung der GI-Fachgruppe 0.1.6 (Peter H. Schmitt, ed.), Universität Karlsruhe, Fakultät für Informatik, Bericht 23/95, pp. 75–78, Juni 1995.

  47. Manna, Z. and Waldinger, R., “Knowledge and Reasoning in Program Synthesis,”Technical Note, 98, Stanford Research Institute, Menlo Park, CA, USA, November 1974.

    Google Scholar 

  48. Manna, Z. and Waldinger, R., “Deductive Synthesis of the Unification Algorithm,” inComputer Program Synthesis Methodologies (Alan W. Biermann and Gérard Guiho, eds.), D. Reidel Publ. Co., pp. 251–307, 1983.

  49. Manna, Z. and Waldinger, R., “Fundamentals of Deductive Program Synthesis,”IEEE Transactions on Software Engineering, 18, 8, pp. 674–704, 1991.

    Article  Google Scholar 

  50. Neugebauer, G.,Pragmatische Programmsynthese, volume 18 of DISKI, Dissertationen zur Künstlichen Intelligenz, infix, 1992.

  51. Pelavin, R. N., “Planning with Simultaneous Actions and External Events,” inReasoning about Plans (James F. Allen, Henry A. Kautz, Richard N. Pelavin, and Josh D. Tenenberg, eds.), chapter 3, Morgan Kaufmann, pp. 127–211. 1991.

  52. Shapiro, E. Y. “An Algorithm That Infers Theories from Facts,” inProc. 7th Intern. Joint Conference on Artificial Intelligence, Vancouver, Canada, pp. 446–451, 1981.

  53. Shapiro, E. Y.,Algorithmic Program Debugging, MIT Press, 1983.

  54. Summers, P. D., “Program Construction from Examples,”Ph. D thesis, Yale University, Dept. Comp. Sci., 1975.

  55. Summers, P. D., “A Methodology for LISP Program Construction from Examples,”Journal of the ACM, 24, 1, pp. 161–175, 1977.

    Article  MATH  MathSciNet  Google Scholar 

  56. Wanke, E., “Algorithms for Graph Problems on BNLC Structured Graphs,”Information and Computation, 94, 1, pp. 93–122, 1989.

    Article  MathSciNet  Google Scholar 

  57. Werling, G.,Produktorientierte automatische Planung von Prüfoperationen bei der robotergestützten Montage, volume 46 of DISKI, Dissertationen zur Künstlichen Intelligenz, infix, 1993.

  58. Wiehagen, R., “Characterization Problems in the Theory of Inductive Inference,” inAutomata, Languages, and Programming, 5th ICALP’78, volume 62 of Lecture Notes in Computer Science, Springer-Verlag, pp. 571–579, 1978.

  59. Wiehagen, R., “From Inductive Inference to Algorithmic Learning Theory,” inProc. 3rd Workshop on Algorithmic Learning Theory (ALT’92) (Shuji Doshita, Koichi Furukawa, Klaus P. Jantke, and Toyaki Nishida, eds.), October 20–22, 1992, Tokyo,volume 743 of Lecture Notes in Artificial Intelligence, Springer-Verlag, pp. 13–24, 1992.

  60. Wilkins, D. E.,Practical Planning: Extending the Classical AI Planning Paradigm., Morgan Kaufmann, 1988.

Download references

Author information

Authors and Affiliations

  1. Institut für Wirtschaftsinformatik, Universität Leipzig, Marschnerstr. 31, 04109, Leipzig, Germany

    Oksana Arnold

  2. Fachbereich IMN, HTWK Leipzig, Postfach 30066, 04251, Leipzig, Germany

    Klaus P. Jantke

Authors
  1. Oksana Arnold
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Klaus P. Jantke
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

The work has been partially supported by the German Federal Ministry for Research and Technology (BMFT) within the Joint Project (BMFT-Verbundprojekt)Wiscon onDevelopment of Methods for Intelligent Monitoring and Control under contract no. 413-4001-01 IW 204 B. Additionally, the second author’s work in learning theory received some support from the German Federal Ministry for Research and Technology (BMFT) within the Joint Project (BMFT-Verbundprojekt)Gosler onAlgorithmic Learning for Knowledge-Based Systems under contract no. 413-4001-01 IW 101 A.

Oksana Arnold: She graduated from Leipzig University of Technology in 1990 with a Master’s Thesis on a rule interpreter for default reasoning. She received her PhD. in Computer Science in 1996 on therapy control for complex dynamic processes within a knowledge-based process supervision and control system. Recently, She works at the University of Leipzig within a research project on information and communication technologies for virtual enterprises. Her main scientific interest is both in knowledge-based process supervision and control, where she did a pioneering work on therapy plan generation, and in flexible information systems for new generation business applications.

Klaus P. Jantke: He graduated from Humboldt University Berlin with a Master’s Thesis in 1975. He received his Ph. D. in Computer Science in 1979 and his Habilitation at Humboldt in 1984. He worked as the Head of a Research Laboratory in Theoretical Computer Science and as a Vice-Director of the Computing Center at Humboldt University. Since 1987, Dr. Jantke is full professor at Leipzig University of Technology. His main research interest is in algorithmic learning theory. Besides this, he contributes to case-based reasoning, where his special interest is in learning issues and in structural similarity, and to knowledge-based process supervision and control, especially to planning. Dr. Jantke is member of the ACM, the EATCS, and the GI.

About this article

Cite this article

Arnold, O., Jantke, K.P. Inductive program synthesis for therapy plan generation. New Gener Comput 15, 27–58 (1997). https://doi.org/10.1007/BF03037559

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03037559

Keywords

Search

Navigation