Uncertainty Handling and Cognitive Biases in Knowledge      Engineering
{'Code': '15030000', 'Directorate': {'Abbreviation': 'TIP', 'LongName': 'Dir for Tech, Innovation, & Partnerships'}, 'Division': {'Abbreviation': 'TI', 'LongName': 'Translational Impacts'}}
{'SignBlockName': 'Kesh S. Narayanan', 'PO_EMAI': None, 'PO_PHON': None}
In this research the principal investigator will explore the               extent to which cognitive biases have an impact on knowledge                    engineering, particularly in the area of uncertainty handling.  He              will demonstrate that not only are experts subject to biases while              solving problems, but that they are also likely to exhibit                      "reporting biases" when they articulate the knowledge they use to               solve problems.  In addition, knowledge engineers will be shown to              be subject to the same biases as domain experts and therefore                   unlikely to catch biases in knowledge elicited from domain experts.             An Automated Knowledge Elicitation Tool (AKET) will be developed                and used in the research effort.
{'FirstName': 'Michael', 'LastName': 'Donnell', 'PI_MID_INIT': 'L', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Michael L Donnell', 'EmailAddress': None, 'PI_PHON': None, 'NSF_ID': '000394497', 'StartDate': '01/01/1989', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Donnell & Associates Incorporated', 'CityName': 'McLean', 'ZipCode': '221028161', 'PhoneNumber': '7034766854', 'StreetAddress': 'PO Box 10161', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Virginia', 'StateCode': 'VA', 'CONGRESSDISTRICT': '11', 'CONGRESS_DISTRICT_ORG': 'VA11', 'ORG_UEI_NUM': None, 'ORG_LGL_BUS_NAME': None, 'ORG_PRNT_UEI_NUM': None}
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{'Code': '5371', 'Text': 'SBIR Phase I'}
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