Enhancements to the theory of network-based planning techniques invented in the late 1950's, along with improved hardware and software environments, have resulted in elegant and powerful tools or planning today's construction projects. However, there are several limitations to these traditional project planning techniques. A fundamental limitation is that they are able to manipulate only the data generated by the planning process, not the knowledge used in generating the project plan. Artificial Intelligence (AI) techniques provide new means to generate plans of actions, and to reason with, and provide explanations from, stored knowledge. However, the potential value of AI-planning systems in the construction domain has been demonstrated only at two ends of a spectrum of generality. Planning systems that can provide useful decision support for a wide range of executive and work package level tasks have yet to be developed. The overall objective of this research is to develop an AI-leveraged planning system for construction projects, by adapting and extending AI techniques for automated planning and knowledge-based expert systems. The resulting system for detailed activity planning and high level project planning will exploit the capability of AI techniques for storing and manipulating knowledge. This AI-based planning research has the potential to extend present knowledge of, and to enhance available technology for construction project planning. Since projects -- which need planning -- are common to most technology-based industries, the result of this research will be of interest to practitioners and researchers in many disciplines.

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Stanford University
Palo Alto
United States
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