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Using artificial intelligence techniques to support project management

Published online by Cambridge University Press:  27 February 2009

Raymond E. Levitt*
Affiliation:
Department of Civil Engineering, Stanford University, Stanford, CA
John C. Kunz
Affiliation:
IntelliCorp, Mountain View, CA
*
Dr Raymond E. Levitt, Associate Professor, Department of Civil Engineering, Terman Engineering Center 298, Stanford University, Stanford, CA, 94305-4020 U.S.A.

Abstract

This paper develops a philosophy for the use of Artificial Intelligence (AI) techniques as aids in engineering project management.

First, we propose that traditional domain-independent, ‘means–and’ planners, may be valuable aids for planning detailed subtasks on projects, but that domain-specific planning tools are needed for work package or executive level project planning. Next, we propose that hybrid computer systems, using knowledge processing techniques in conjunction with procedural techniques such as decision analysis and network-based scheduling, can provide valuable new kinds of decision support for project objective-setting and project control, respectively. Finally we suggest that knowledge-based interactive graphics, developed for providing graphical explanations and user control in advanced knowledge processing environments, can provide powerful new kinds of decision support for project management.

The first claim is supported by a review and analysis of previous work in the area of automated AI planning techniques. Our experience with PLATFORM I, II and III, a series of prototype AI-leveraged project management systems built using the IntelliCorp Knowledge Engineering Environment (KEE™), provides the justification for the latter two claims.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1987

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References

Arditi, D. 1983. Diffusion of network planning in construction. Journal of Construction Engineering and Management 109 (1), 112.Google Scholar
Bell, C. E. and Taste, A. 1985. Use and justification of algorithms for managing temporal knowledge in O-plan. AIAI-TR-6, Artificial Intelligence Applications Institute, University of Edinburgh, Edinburgh, U.K.Google Scholar
Carr, R. I. 1971. Synthesis of uncertainty in construction planning. Ph.D. dissertation, Department of Civil Engineering, Stanford University, Stanford, CA, U.S.A.Google Scholar
Chan, W. T. 1986 Logical programming for managing construction-based engineering design. Ph.D. dissertation, Department of Civil Engineering, Stanford University, Stanford, CA, U.S.A.Google Scholar
Chandra, N. and Marks, D. H. 1986. Intelligent use of constraints for activity scheduling. In: Sriram, D. and Adey, R., Eds, Proceedings of the First International Conference on Applications of Artifical Intelligence to Engineering Problems, Southampton University, Southampton, U.K. pp. 369382.CrossRefGoogle Scholar
Cohen, P. and Feigenbaum, E. 1982. Planning and problem solving. In: The Handbook of Artificial Intelligence, volume 3, Chapter XV, Los Altos: William Kaufmann Inc.Google Scholar
Currie, K. and Tate, A. 1985. O-plan—control in the open planning architecture, AIAI-TR-12, Artificial Intelligence Applications Institute, University of Edinburgh, Edinburgh, U.K.Google Scholar
Davis, E. W. 1974. CPM use in top 400 construction firms. ASCE Journal of the Construction Division 100 (CO1), 3949CrossRefGoogle Scholar
DeKleer, J. 1986. An assumption-based TMS. Artificial Intelligence. 28 (1).Google Scholar
DeLaGarza, J. M. and Ibbs, W. 1987. Issues in construction scheduling knowledge representation. Proceedings of the CIB W-65 Symposium. London: E. and F. N. Spon.Google 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, 189208.CrossRefGoogle Scholar
Fondahl, J. W. 1961. A noncomputer approach to the critical path method for the construction industry. TR 9, Department of Civil Engineering, Stanford University, Stanford, CA, U.S.A.Google Scholar
Genesereth, M. R. and Nilsson, N. J. 1987. Logical Foundations of Artificial Intelligence. Los Altos: Morgan Kaufmann.Google Scholar
Gray, C. 1986. Intelligent construction time and cost analysis. Construction Management and Economics, no. 4, 135150.Google Scholar
Hayes-Roth, B. 1985. A blackboard architecture for control. Artificial Intelligence 26, 251321.CrossRefGoogle Scholar
Hayes-Roth, B., Garvey, A., Johnson, M. V., and Hewett, M. 1986 a. A layered environment for reasoning about action. KSL-86–38, Department of Computer Science, Stanford University, Stanford, CA, U.S.A.Google Scholar
Hayes-Roth, B., Buchanan, B. G.Lichtarge, O., Hewett, M., Altman, R., Brinkley, J., Cornelius, C.Duncan, B., and Jardetzky, O. 1986 b. PROTEAN: Deriving protein structure from constraints. Proceedings of the AAAI.Google Scholar
Hendrickson, C., Martinelli, D., and Rehak, D 1987 a. Hierarchical rule-based activity duration estimation. Journal of Construction Engineering and Management 113 (2), 228301.CrossRefGoogle Scholar
Hendrickson, C., Zozaya-Gorostiza, C., Rehak, D., Baracco-Miller, E., and Lim, P. 1987 b An expert system architecture for construction planning. EDRC-12−07−87, Carnegie Mellon University.Google Scholar
Koo, C. 1986. Intelligent process planning for assembly manufacturing. Working paper, Intelligent Systems, Management Systems Engineering Program, Stanford University, Stanford, CA, U.S.A.Google Scholar
Koo, C. 1987. Synchronizing plans among intelligent agents via communications. PhD dissertation, Department of Civil Engineering, Stanford University, Stanford, CA, under preparation.Google Scholar
Kunz, J., Bonura, T., Stelzner, M., and Levitt, R. 1986. Contingent analysis for project management using multiple worlds. In: Sriram, D. and Adey, R., Eds, Proceedings of the First International Conference on Applications of Artificial Intelligence to Engineering Problems, Southampton University, Southampton, U.K. pp. 707718.Google Scholar
Kunz, J. 1987. Model based reasoning. Unpublished manuscript.Google Scholar
Levitt, R. E. and Kunz, J C. 1985. Using knowledge of construction and project management for automated schedule updating. Project Management Journal 16 (5), 5776.Google Scholar
Luria, M. 1987. Concerns: a means of identifying potential plan failures. In: Proceedings of the Third IEEE Conference on Al Applications, Orlando, Florida.Google Scholar
Mittal, S., Dym, C. L., and Morjaria, M. 1985. PRIDE: An expert system for the design of paper handling systems. In: Dym, C. L., Ed., Applications of Knowledge-Based Systems to Engineering Analysis and Design, ASME Proceedings Vol. AD-10, 99116.Google Scholar
Moder, J. J., Phillips, C. R., and Davis, E. W. 1983. Project Management with CPM, PERT and Precedence Diagramming. New York: Van Nostrand Reinhold.Google Scholar
Morris, P. and Nado, R. 1986. Representing actions with an assumption-based truth maintenance system. Proceedings of the AAAI.Google Scholar
Nay, L. B. and Logcher, R. D. 1985. Proposed operation of an expert system for analyzing construction project risks. TR 5, order #CCRE-85-Z, Department of Civil Engineering, M I.T.Google Scholar
Niwa, K. and Sasaki, K. 1983. A new project management system approach: The ‘Know-How’ based project management system. Project Management Journal 14 (1), 6572.Google Scholar
O'Connor, M. J., DeLaGarza, J. M., and Ibbs, C. W. 1986. An expert system for construction schedule analysis. In: Kortem and Maher, Expert Systems in Civil Engineering ASCE.Google Scholar
Paulson, B. C. 1971. Man–computer concepts For Project Management. TR 148, Department of Civil Engineering, Stanford University, Stanford, CA, U.S.A.Google Scholar
Popplestone, R. J. 1984. The application of artificial intelligence techniques to design systems. International Symposium on Design and Synthesis. Tokyo, Japan. Society of Precision EngineersGoogle Scholar
Sacerdoti, E. D. 1973. Planning in a hierarchy of abstraction spaces. IJCAI, 412422, Palo Alto, CA.Google Scholar
Sacerdoti, E. D. 1975. The non-linear nature of plans. IJCAI, 206214, Tbilisi, U.S.S.R.Google Scholar
Sathi, A., Morton, T. E., and Roth, S. F. 1986. Callisto: an intelligent project management system. Al Magazine 7 (5), 3452.Google Scholar
Stefik, M. J. 1981. Planning with constraints. Artificial Intelligence 16, 111–140.CrossRefGoogle Scholar
Tate, A. 1975. Interacting goals and their use. IJCAI, 215218, Tbilisi, USSR.Google Scholar
Tate, A. 1976. Project planning using a hierarchic non-linear planner. Research report 25 Department of Artificial Intelligence, University of Edinburgh, Edinburgh, U.K.Google Scholar
Tate, A. 1977. Generating project networks. IJCAI, 888893, Boston, MA, U.S.A.Google Scholar
Tate, A. 1985. A review of knowledge-based planning techniques. Knowledge Engineer's Review 1 (2), British Computer Society Specialist Group on Expert Systems.Google Scholar
Teicholz, P. 1986. Personal communication with Dr. Paul Teicholz. Manager of Corporate Information Systems. Guy F. Atkinson Co., South San Francisco, CA, U.S.A.Google Scholar
Vere, S. 1983. Planning in time: windows and durations activities and goals. IEEE Transactions on Pattern Analysis and Machine Intelligence 5 (3), 246267.CrossRefGoogle ScholarPubMed
Waldinger, R. 1975. Achieving several goals simultaneously. Technical note 107, Stanford Research Institute—AI Center, Menlo Park, CA, U.S.A.Google Scholar
Wideman, R. M. 1986. The PMBOK report: PMI Body of Knowledge Standards. Project Management Journal 17 (3), 1524, (Special Summer issue, Project Management Body of Knowledge).Google Scholar
Wilkins, D E. 1984. Domain-independent planning: representation and plan generation. Artificial Intelligence 22, 269301.CrossRefGoogle Scholar
Woolery, J. C. and Crandall, K. C. 1983. Stochastic network model for planning scheduling. Journal of Construction Engineering and Management 109 (3), 342354.CrossRefGoogle Scholar