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Journal of Systems Integration

, Volume 4, Issue 3, pp 219–241 | Cite as

Formal model of single agent planning situations

  • Richard Mayer
  • Madhav Erraguntla
  • Christopher Menzel
  • Jyh Chen Hwang
Article

Abstract

Planning is perhaps the single most important activity in any domain, be it a business enterprise, academics or personal life. However most efforts in a Artificial Intelligence to provideintelligent planners have met with a very limited success. Successful, non-trivial applications are limited to very domain and situation specific cases. A generic component which can serve as a schema or template from which planning aids to any specific domain can be instantiated would be very much useful in building specific planning applications. A formal method for characterizing the needed capabilities of a generic component is required. In this paper we present such a method and illustrate its use through analysis of simple agent planiing situations. The result of this analysis is presented in the form of an ontology, and a formal languags with an associated model theoretic semantics. The results presented can be used as a framework for benchmarking to compare and discriminate between the knowledge/information representation capabilites of different software systems. The results also serve as a model for formalizing the description of a particular class of engineering, business or manufacturing planning activities. The focus of this paper is on thekowledge andinformation that must berepresented rather than onplan generation strategies.

Keywords

Single agent planning knowledge representation ontology formalization 

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References

  1. 1.
    J. F. Allen and A. K. Johannes. Planning using a temporal model. InProceedings of the Eigth International Joint Conference on Artificial Intelligence, pages 741–747, Karlsruhe, West Germany, 1983.Google Scholar
  2. 2.
    J. Barwise and J. Perry,Situations and Attitudes. The MIT Press, Cambridge, 1983.Google Scholar
  3. 3.
    K. Devlin.Logic and Information, Volume I: Situation Theory. Cambridge University Press, 1991.Google Scholar
  4. 4.
    R. E. Fikes, P. E. Hart and N. J. Nilson. Learning and executing generalized robot plans. InReading in Artificial Intelligence, pages 231–249, Morgan Kaufmann Publishers, Inc., 1981.Google Scholar
  5. 5.
    M. P. Georgeff. Planning, InReadings in Planning, pages 5–25, Morgan Kaufmann Publishers, Inc., 1990.Google Scholar
  6. 6.
    S. B. Joshi, R. A. Wysk, A. Jones. A scalable architecture for CIM shop floor control. InProceedings of CIMCON 90, pages 21–33. National Institute of Standards and Technology, Gaithersburg, MD, May 1990.Google Scholar
  7. 7.
    R. J. Mayer, C. M. Menzel, P. S. deWitte,IDEFI method formalization. Knowledge Based Systems, Inc. College Station, TX, 1991.Google Scholar
  8. 8.
    C. P. Menzel and R. J. Mayer,Theoretical Foundations for Information Representation and Constraint Specification. Knowledge Based Systems, Inc., College Station, TX, 1990.Google Scholar
  9. 9.
    C. P. Menzel and R. J. Mayer,IDEF5 Ontology Description Capture Method. Knowledge Based Systems, Inc., College Station, TX, 1991.Google Scholar
  10. 10.
    C. P. Menzel. The importance of mathematical formalization for the advancement of information modelling technology. InProceedings of IDEF Users Group, Fort Worth. TX. 1991.Google Scholar
  11. 11.
    C. P. Menzel, R. J. Mayer, and L. K. Sanders, Representation, information flow, and model Integration. In C. Petrie, editor,Proceedings of the International Conference of Engineering Integration and Modeling Technology, MIT Press, Austin, TX, Cambridge, 1992.Google Scholar
  12. 12.
    C. P. Menzel, R. J. Mayer, D. D. Edwards. IDEF3 process descriptions and their semantics. In A. Kusiac and C. Dagli, editor.Forthcoming in Intelligent Systems in Design and Manufawcturing. ASME Press, New York, 1993.Google Scholar
  13. 13.
    N. J. Nilsson,Principles of Artificial Intelligence. Tioga Publishing Company, 1980.Google Scholar
  14. 14.
    S. L. Tanimoto.The Elements of Artificial Intelligence: An Introduction using COMMON LISP. Computer Science Press, 1987.Google Scholar
  15. 15.
    R. Waldinger. Achieving several goals simultaneously. InReadings in Artificial Intelligence. Morgan Kaufmann Publishers, Inc., 1981, pages 250–271.Google Scholar
  16. 16.
    R. Wilensky. Meta-planning: Representing and using knowledge about planning in problem solving and natural language understanding.Cognitive Science. 5: 197–233, 1981.Google Scholar

Copyright information

© Kluwer Academic Publishers 1994

Authors and Affiliations

  • Richard Mayer
    • 1
  • Madhav Erraguntla
    • 1
  • Christopher Menzel
    • 1
  • Jyh Chen Hwang
    • 1
  1. 1.Department of Industrial EngineeringTexas A&M UniversityCollege Station

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