A Hybrid AI-based Project Planning System
{'Code': '07030000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'CMMI', 'LongName': 'Div Of Civil, Mechanical, & Manufact Inn'}}
{'SignBlockName': 'Ken Chong', 'PO_EMAI': None, 'PO_PHON': None}
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.
{'FirstName': 'Raymond', 'LastName': 'Levitt', 'PI_MID_INIT': 'E', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Raymond E Levitt', 'EmailAddress': 'Ray.Levitt@Stanford.edu', 'NSF_ID': '000451948', 'StartDate': '10/15/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Stanford University', 'CityName': 'STANFORD', 'ZipCode': '943052004', 'PhoneNumber': '6507232300', 'StreetAddress': '450 JANE STANFORD WAY', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '16', 'CONGRESS_DISTRICT_ORG': 'CA16', 'ORG_UEI_NUM': 'HJD6G4D6TJY5', 'ORG_LGL_BUS_NAME': 'THE LELAND STANFORD JUNIOR UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '1442', 'Text': 'CONSTRUCTION AND INFRASTRUCTUR'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8716608.xml'}