Pragmatic Approaches to Reasoning Under Uncertainty         (Computer and Information Science)
{'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}
This research investigates pragmatic approaches to                         computer-based probabilistic reasoning systems.  In the past,                   artificial intelligence researchers have often avoided probability              theory for reasoning with uncertainty because of a common perception            that the application of probability theory is invariably associated             with a commitment to intractable algorithms and inordinate                      knowledge-acquisition time.  The development of efficient                       algorithmic and assessment techniques would allow investigators to              apply a theoretically justified model of belief entailment to                   complex problems.  As a result, the goals in this research are (1)              to develop techniques for using knowledge about problem-solving                 tradeoffs to optimize dynamically the value of computer performance             to the user, (2) to construct efficient algorithms for probabilistic            reasoning, and (3) to investigate pragmatic techniques for the                  elicitation of knowledge from experts. Two existing diagnostic                  systems are testbeds for this research, which is directly applicable            to many areas of science and engineering.                                            Although the techniques of artificial intelligence are being               used to provide decision support tools ("expert systems") in a                  variety of settings within science, technology, and industry,                   several fundamental research problems remain.  One significant                  concern is the lack of well-developed methods for dealing with the              uncertainty that characterizes many problem-solving tasks. For                  example, in medicine or geological exploration, experts must infer              the state of the process they are investigating (e.g., a patient's              disease or a possible mineral deposit) by interpreting indirect                 measurements that generally provide only partial support for                    hypotheses.  Most available tools deal with such uncertainty by                 using ad hoc methods whose applicability and limitations are poorly             understood.  By developing practical methods for using formal                   probability theory in such systems, this research replaces ad hoc               techniques with approaches for which the underlying assumptions and             limitations can be demonstrated analytically and considered during              system construction and use.
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{'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}
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{'Code': '6856', 'Text': 'ARTIFICIAL INTELL & COGNIT SCI'}
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