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.
Similar content being viewed by others
References
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.
J. Barwise and J. Perry,Situations and Attitudes. The MIT Press, Cambridge, 1983.
K. Devlin.Logic and Information, Volume I: Situation Theory. Cambridge University Press, 1991.
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.
M. P. Georgeff. Planning, InReadings in Planning, pages 5–25, Morgan Kaufmann Publishers, Inc., 1990.
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.
R. J. Mayer, C. M. Menzel, P. S. deWitte,IDEFI method formalization. Knowledge Based Systems, Inc. College Station, TX, 1991.
C. P. Menzel and R. J. Mayer,Theoretical Foundations for Information Representation and Constraint Specification. Knowledge Based Systems, Inc., College Station, TX, 1990.
C. P. Menzel and R. J. Mayer,IDEF5 Ontology Description Capture Method. Knowledge Based Systems, Inc., College Station, TX, 1991.
C. P. Menzel. The importance of mathematical formalization for the advancement of information modelling technology. InProceedings of IDEF Users Group, Fort Worth. TX. 1991.
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.
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.
N. J. Nilsson,Principles of Artificial Intelligence. Tioga Publishing Company, 1980.
S. L. Tanimoto.The Elements of Artificial Intelligence: An Introduction using COMMON LISP. Computer Science Press, 1987.
R. Waldinger. Achieving several goals simultaneously. InReadings in Artificial Intelligence. Morgan Kaufmann Publishers, Inc., 1981, pages 250–271.
R. Wilensky. Meta-planning: Representing and using knowledge about planning in problem solving and natural language understanding.Cognitive Science. 5: 197–233, 1981.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Mayer, R., Erraguntla, M., Menzel, C. et al. Formal model of single agent planning situations. Journal of Systems Integration 4, 219–241 (1994). https://doi.org/10.1007/BF01976184
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF01976184