Logical Foundations for Inheritance Theory and Knowledge    Update (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': 'Howard Moraff', 'PO_EMAI': None, 'PO_PHON': None}
Continuing research that has been supported since April, 1986,             under NSF Grant No IST-8516313, we are extending and deepening our              investigation of interactions between logical theory, on the one hand,          and inheritance and knowledge update in semantic networks on the                other. The research consists of both theoretical work and experiments           on implementations, and includes a number of interrelated projects:               (1) Logic analysis of knowledge update as applied to semantic                 networks; we will continue to explore the theory of conditional logic           as a framework for dealing with knowledge update in semantic networks,          and to compare the resulting accounts of update with those proposed by          other computer scientists and logicians.                                          (2) Articulating the design space for nonmonotonic inheritance                reasoners; building on our foundational investigations, we will begin           to work toward a mathematical classification of nonmonotonic                    inheritance reasoners, and to establish some general theorems in this           area.                                                                             (3) The theory of nonmonotonic inheritable relations and roles; we            will extend our analyses of inheritance in nonmonotonic semantic                networks to richer network representation languages, including at               least the resources for describing relations and roles.                           (4) The theory of mixed semantic networks; we will extend our                 analyses of inheritance to semantic networks in which both strict               and defeasible information is mixed together in a single                        representational structure.                                                       (5) Nonmonotonic hybrid reasoners; we will explore validated                  systems that employ both nonmonotonic network-based reasoning and               theorem proving in information retrieval.                                          The broader significance of this work is that it addresses the               urgent need to design mechanical systems capable of intelligent                 reasoning in a variety of expert tasks.  Such systems will need to              store large quantities of complex information in a form that is                 efficiently accessible by mechanized reasoning processes, and also              structured in a way that is intelligible to domain experts so that it           can be validated and improved.                                                     Research in Artificial Intelligence has shown that there is a                conflict between two desirable goals for systems of knowledge                   representation: they must be powerful enough to express complex                 concepts, but at the same time they must reason quickly and allow               efficient updates. Designing computer systems that can meet both of             these goals constitutes a large-scale research project; new                     theoretical concepts and experiments with working reasoning systems             must both be developed. Our project will combine theory and                     experiments to produce systems of knowledge representation and                  knowledge update. The dual approach will produce knowledge and                  reasoning systems which not only work better, but which are grounded            in a sound theoretical analysis.
[{'FirstName': 'Richmond', 'LastName': 'Thomason', 'PI_MID_INIT': 'H', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Richmond H Thomason', 'EmailAddress': 'rich@thomason.org', 'NSF_ID': '000311208', 'StartDate': '06/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}, {'FirstName': 'David', 'LastName': 'Touretzky', 'PI_MID_INIT': 'S', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'David S Touretzky', 'EmailAddress': 'dst@cs.cmu.edu', 'NSF_ID': '000191816', 'StartDate': '06/01/1987', 'EndDate': None, 'RoleCode': 'Co-Principal Investigator'}]
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{'Name': 'MPC Corporation', 'CityName': 'PITTSBURGH', 'StateCode': 'PA', 'ZipCode': '152133815', 'StreetAddress': '5000 FORBES AVE', 'CountryCode': 'US', 'CountryName': 'United States', 'StateName': 'Pennsylvania', 'CountryFlag': '1', 'CONGRESSDISTRICT': '12', 'CONGRESS_DISTRICT_PERF': 'PA12'}
{'Code': '6856', 'Text': 'ARTIFICIAL INTELL & COGNIT SCI'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8700705.xml'}