Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-26T12:05:00.486Z Has data issue: false hasContentIssue false

Activity aggregation in model-based AI planning systems

Published online by Cambridge University Press:  27 February 2009

Graham Winstanley
Affiliation:
Department of Computing, University of Brighton, UK The Center for Integrated Facility Engineering, Stanford University, CA 94305-4020, USA
Kunito Hoshi
Affiliation:
Kumagai Gumi Co., Ltd, Tokyo, Japan The Center for Integrated Facility Engineering, Stanford University, CA 94305-4020, USA

Abstract

When model-based planning systems are scaled up to deal with full-sized industrial projects, the resulting complexity in the project-specific model and production plan can create serious problems, not only in dealing with such complexity computationally, but also in user-acceptance. In the model-based planning system described in this paper, activities are dynamically generated, inherently at the detailed level of individual physical components. However, it is possible to intelligently group together collections of components which would be common to realistic work packages, and hence schedule on the basis of virtual components existing within an abstraction hierarchy. This paper describes a technique of project planning within an integrated design/planning system, which exploits fundamental knowledge of engineered systems and provides powerful and flexible planning functionality.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1993

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chapman, D. 1987. Planting for conjunctive goals, Artificial Intelligence 32, 333377.CrossRefGoogle Scholar
Darwiche, A., Levitt, R. E. and Hayes-Roth, B. 1989. OARPLAN: generating project plans by reasoning about objects, actions and resources, Artificial Intelligence for Engineering Design, Analysis and Manufacturing 2(3), 169181.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, 198208.CrossRefGoogle Scholar
Fischer, M. A. 1991. Constructability input to preliminary design of reinforced concrete structures, CIFE Technical Report #64, Center for Integrated Facility Engineering, Stanford University, CA.Google Scholar
Hayes-Roth, B. and Hewett, M. 1987. Building systems in the BB* environment. In Blackboard Systems, ed. Englemore, R., and Morgan, A.London: Addison-Wesley.Google Scholar
Hendrickson, C., Zozaya-Gorostiza, C., Rehak, D., Baracco-Miller, E. and Lim, P. 1987. Expert system for construction planning, ASCE J. of Computing in Civil Engineering 1(4), 253269.CrossRefGoogle Scholar
Howard, H. C., Levitt, R. E., Paulson, B. C., Pohl, J. G., and Tatum, C. B. 1989. Computer integration-reducing fragmentation in AEC industry, ASCE Journal of Computing in Civil Engineering, 3(1), January 1989, 1833.CrossRefGoogle Scholar
Ito, K., Ueno, Y., Levitt, R. E. and Darwiche, A. 1989. Linking knowledge-based systems to CAD-design data with an object-oriented building project model. CIFE Working Paper #7, Stanford University, CA, USA.Google Scholar
Kartam, N. A. and Levitt, R. E. 1990. Intelligent planning of construction projects with repeated cycles of operation, ASCE Journal of Computing in Civil Engineering 4(2), 155176.CrossRefGoogle Scholar
Marshall, G., Barber, T. J. and Boardman, J. T. 1987. A methodology for modeling a project management control environment, IEEE Proceedings 134, 287300.CrossRefGoogle Scholar
Sacerdoti, E. D. 1973. Planning in a hierarchy of abstraction spaces, Proceedings of IJCAI-73, Palo Alto, CA.Google Scholar
Sacerdoti, E. D. 1975. The nonlinear nature of plans, Advance Papers, IJCAI-75, Tbilisi, U.S.S.R.Google Scholar
Stefik, M. J. 1981 a. Planning with constraints (Molgen: Part 1), Artificial Intelligence 16(2), 111140.CrossRefGoogle Scholar
Stefik, M. J. 1981 b. Planning and meta-planning (Molgen: Part 2), Artificial Intelligence 16(2), 141170.CrossRefGoogle Scholar
Tate, A. 1976. Project planning using a hierarchical nonlinear planner. Department of Artificial Intelligence Research Report No. 25, University of Edinburgh, Edinburgh, U.K.Google Scholar
Wilkins, D. E. 1988. Practical Planning: Extending The Classical AI Planning Paradigm. Los Altos, CA: Morgan Kaufmann.Google Scholar
Winstanley, G., Boardman, J. T. and Kellett, J. M. 1990. An intelligent planning assistant in the manufacture of flight simulators. Proceedings of the ACME Research Conference, University of Birmingham, UK, 2nd-5th September.Google Scholar
Winstanley, G., Kellett, J. M., Best, J. T. and Teskey, F. N. 1989. IDEAS—for expert systems. In POP-11 Comes of Age: The Advancement of an AI Language, ed. Anderson, J., Ellis Horwood Press.Google Scholar
Winstanley, G., Chacon, M. and Levitt, R. E. 1992. The application of model-based planning technology to full-scale projects. CIFE Technical Report #66, Center for Integrated Facility Engineering, University of Stanford, CA.Google Scholar
Zozoya-Gorostiza, C., Hendrickson, C. and Rehak, D. 1989. Knowledge-based Process Planning for Construction and Manufacturing, London: Academic Press.Google Scholar