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OARPLAN: Generating project plans by reasoning about objects, actions and resources

Published online by Cambridge University Press:  27 February 2009

Adnan Darwiche ,
Raymond E. Levitt  and
Barbara Hayes-Roth
Adnan Darwiche
Affiliation:
Department of Computer Science and Civil Engineering, Stanford University, Stanford, CA 94305, U.S.A.
Raymond E. Levitt
Affiliation:
Department of Civil Engineering, Stanford University, Stanford, CA 94305, U.S.A.
Barbara Hayes-Roth
Affiliation:
Center for Integrated Facility Engineering, Stanford University, Stanford, CA 94305, U.S.A.
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Abstract

This paper describes OARPLAN, a prototype planning system that generates construction project plans from a description of the objects that comprise the completed facility. OARPLAN is based upon the notion that activities in a project plan can be viewed as intersections of their constituents: objects, actions and resources. Planning knowledge in OARPLAN is represented as constraints based on activity constituents and their interrelationships; the planner functions as a constraint satisfaction engine that attempts to satisfy these constraints. The goal of the OARPLAN project is to develop a planning shell for construction projects that (i) provides a natural and powerful constraint language for expressing knowledge about construction planning, and (ii) generates a facility construction plan by satisfying constraints expressed in this language.

To generate its construction plan, OARPLAN must be supplied with extensive knowledge about construction objects, actions and resources, and about spatial, topological, temporal and other relations that may exist between them. We suggest that much of the knowledge required to plan the construction of a given facility can be drawn directly from a three-dimensional CAD model of the facility, and from a variety of databases currently used in design and project management software. In the prototype OARPLAN system, facility data must be input directly as frames. However, we are collaborating with database researchers to develop intelligent interfaces to such sources of planning data, so that OARPLAN will eventually be able to send high level queries to an intelligent database access system without regard for the particular CAD system in which the project was designed.

We begin by explaining why classical AI planners and domain specific expert system approaches are both inadequate for the task of generating construction project plans. We describe the activity representation developed in OARPLAN and demonstrate its use in producing a plan of about 50 activities for a steel-frame building, based on spatial and topological constraints that express structural support, weather protection and safety concerns in construction planning. We conclude with a discussion of the research issues raised by our experiments with OARPLAN to date.

Type
Research Article
Information
AI EDAM , Volume 2 , Issue 3 , August 1988 , pp. 169 - 181
Copyright
Copyright © Cambridge University Press 1988

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References

Bremdal, B. A. 1987. Control issues in knowledge-based planning system for ocean engineering tasks. Proceedings of 3rd International Expert Systems Conference, pp. 21–36.Google Scholar
Chapman, D. 1987. Planning for conjunctive goals. Artificial Intelligence, 32, 333–377.CrossRefGoogle Scholar
Fikes, R. E. and Nilsson, N. J. 1971. STRIPS: A new approach to the application of theorem proving to problem solving Artificial Intelligence, 2, 198–208.CrossRefGoogle Scholar
Fox, M. S. and Smith, S. F. 1984. ISIS—a knowledge-based system for factory scheduling. Expert Systems, 1, 25–49.CrossRefGoogle Scholar
Hayes-Roth, B., Buchanan, B. G., Lichtarge, O., Hewett, M., Altman, R., Brinkley, J., Cornelius, C., Duncan, B. and Jardetzky, O. 1986. PROTEAN: deriving protein structure from constraints Proceedings of the AAAI.Google Scholar
Hayes-Roth, B. and Hewett, M. 1987. Building systems in the BB* environment. In: R., Engelmore and A.M, organ eds, Blackboard Systems. London: Addison-Wesley.Google Scholar
Hendrickson, C. 1987. Expert system for construction planning. ASCE Journal of Computing, 1, 253–269.Google Scholar
Howard, H. C. and Rehak, D. R. 1988. KADBASE: a prototype expert system-database interface for engineering systems. IEEE Expert.Google Scholar
Howard, H. C., Levitt, R. E., Paulson, B. C., Pohl, J. G. and Tatum, C. B. 1989. Computer-integrated design and construction: reducing fragmentation in the AEC industry. Journal of Computing in Civil Engineering, ASCE 3, 18–32.CrossRefGoogle Scholar
Lansky, A. 1988. Localized event-based reasoning for multiagent domains. Computational Intelligence.CrossRefGoogle Scholar
Levitt, R. E. and Kunz, J. C. 1987. Using artificial intelligence techniques to support project management. Journal of Artificial Intelligence in Engineering, Design, and Manufacturing 1, 3–24.CrossRefGoogle Scholar
Levitt, R. E., Kartam, N. A. and Kunz, J. C. 1988. Artificial intelligence techniques for generating construction project plans. ASCE Journal of Construction Engineering and Management.CrossRefGoogle Scholar
Marshall, G., Barber, T. J. and Boardman, J. T. 1987. Methodology for modelling a project management control environment. IEE Proceedings, 134, 287–300.CrossRefGoogle Scholar
Marshall, G., 1988. PhD Thesis in progress, Information Technology Research Institute, Brighton Polytechnic, Brighton, U.K.Google Scholar
Navinchandra, D., Sriram, D. and Logcher, R. 1988. GHOST: a project network generator. Journal of Computing in Civil Engineering, ASCE, 2, 239–254.CrossRefGoogle Scholar
Sacerdoti, E.D. 1975. The nonlinear nature of plans, in: Advance Papers IJCAI-75, pp. 206–214.Google Scholar
Sathi, A., Fox, M. and Greenberg, M. 1988. Representation of activity knowledge for project management. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-7, 531–551.Google Scholar
Stefik, M. 1981 a. Planning with constraints (MOLGEN: Part 1) Artificial Intelligence, 16, 111–140.CrossRefGoogle Scholar
Stefik, M. 1981 b. Planning and meta-planning (MOLGEN: Part 2) Artificial Intelligence, 16, 141–170.CrossRefGoogle Scholar
Tate, A. 1976. Project planning using a hierarchic nonlinear planner. Department of Artificial Intelligence Research Rept. No. 25, University of Edinburgh.Google Scholar
Tommelein, I., Johnson, M., Hayes-Roth, B. and Levitt, R. 1987. SIGHTPLAN—a blackboard expert system for the layout of temporary facilities on construction sites. In: Gero, J. S., Ed. Computer-Aided Design, pp. 153–167. Amsterdam: North Holland.Google Scholar
Wilkins, D. E. 1984. Domain-independent planning: representation and plan generation. Artificial Intelligence 22, 269–301.CrossRefGoogle Scholar
Wilkins, D. E. 1988. Practical Planning: Extending The Classical AI Planning Paradigm. Los Angeles: Morgan Kauffmann.Google Scholar
Zozoya-Gorostiza, C., Hendrickson, C. and Rehak, D. 1989. Knowledge-based process planning for Construction and Manufacturing. London: Academic Press.Google Scholar