Computational Models of Uncertainty Management and          Decision Making
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
{'SignBlockName': 'Jolita D. Middleton', 'PO_EMAI': None, 'PO_PHON': None}
A Complete approach to uncertainty management requires support                  for interactive and incremental problem formulation, inference,                 hypothesis ranking, and decision making.  Current approaches                    concentrate primarily on inference, and provide little or no                    support for the larger issues in reasoning under uncertainty.                   This proposal presents a plan for research into the use of                      multiple representations of uncertainty as the basis of a                       complete computational foundation for resource.bounded reasoning                and decision.making under uncertainty.  The research is organized               into two phases.  The first phase is continued development of a                 combined symbolic/numeric representation for small.scale problem                model formulation and evaluation.  This framework provides basic                capabilities to support incremental model construction,                         evaluation, and revision, but does not address issues of resource               bounding which become critical in reasoning with large.scale                    models.  A second phase of the proposed research will address                   problems which are intractable for the small.scale model                        approach, by developing representations and evaluation strategies               for decomposing a large.scale model into a set of small.scale                   models.
{'FirstName': 'Bruce', 'LastName': "D'Ambrosio", 'PI_MID_INIT': 'D', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': "Bruce D D'Ambrosio", 'EmailAddress': 'bruce.dambrosio@gmail.com', 'NSF_ID': '000168855', 'StartDate': '05/15/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Oregon State University', 'CityName': 'CORVALLIS', 'ZipCode': '973318655', 'PhoneNumber': '5417374933', 'StreetAddress': '1500 SW JEFFERSON AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Oregon', 'StateCode': 'OR', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'OR04', 'ORG_UEI_NUM': 'MZ4DYXE1SL98', 'ORG_LGL_BUS_NAME': 'OREGON STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '6856', 'Text': 'ARTIFICIAL INTELL & COGNIT SCI'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1988&All=true', 'xml': '8821660.xml'}