Reasoning from Incomplete Knowledge in a Procedural Deduction System

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6898

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DC Field	Value	Language
dc.creator	Moore, Robert Carter	-
dc.date	2004-10-20T20:05:41Z	-
dc.date	2004-10-20T20:05:41Z	-
dc.date	1975-12-01	-
dc.date.accessioned	2013-10-09T02:47:29Z	-
dc.date.available	2013-10-09T02:47:29Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AITR-347	-
dc.identifier	http://hdl.handle.net/1721.1/6898	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	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.	-
dc.format	10580006 bytes	-
dc.format	8308773 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AITR-347	-
dc.title	Reasoning from Incomplete Knowledge in a Procedural Deduction System	-
Appears in Collections:	MIT Items

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