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On Oraclizable Networks and Kahn's Principle

dc.contributor.authorRussell, James R.en_US
dc.date.accessioned2007-04-23T17:39:07Z
dc.date.available2007-04-23T17:39:07Z
dc.date.issued1989-10en_US
dc.description.abstractIn this paper we investigate generalizations of Kahn's principle to nondeterministic dataflow networks. Specifically, we show that for the class of "oraclizable" networks a semantic model in which networks are represented by certain sets of continuous functions is fully abstract and has the fixed-point property. We go on to show that the oraclizable networks are the largest class representable by this model, and are a proper superclass of the networks implementable with the infinity fair merge primitive. Finally, we use this characterization to show that infinity fair merge networks and oraclizable networks are proper subclasses of the networks with Egli-Milner monotone input-output relations.en_US
dc.format.extent1267489 bytes
dc.format.extent291322 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR89-1046en_US
dc.identifier.urihttps://hdl.handle.net/1813/6846
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleOn Oraclizable Networks and Kahn's Principleen_US
dc.typetechnical reporten_US

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