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Reasoning from Incomplete Knowledge in a Procedural Deduction System

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dc.contributor.author Moore, Robert Carter en_US
dc.date.accessioned 2004-10-20T20:05:41Z
dc.date.available 2004-10-20T20:05:41Z
dc.date.issued 1975-12-01 en_US
dc.identifier.other AITR-347 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/6898
dc.description.abstract One very useful idea in AI research has been the notion of an explicit model of a problem situation. Procedural deduction languages, such as PLANNER, have been valuable tools for building these models. But PLANNER and its relatives are very limited in their ability to describe situations which are only partially specified. This thesis explores methods of increasing the ability of procedural deduction systems to deal with incomplete knowledge. The thesis examines in detail, problems involving negation, implication, disjunction, quantification, and equality. Control structure issues and the problem of modelling change under incomplete knowledge are also considered. Extensive comparisons are also made with systems for mechanica theorem proving. en_US
dc.format.extent 10580006 bytes
dc.format.extent 8308773 bytes
dc.format.mimetype application/postscript
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.relation.ispartofseries AITR-347 en_US
dc.title Reasoning from Incomplete Knowledge in a Procedural Deduction System en_US


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