Understanding in Incomplete Worlds

dc.creator	Rosenberg, Steven
dc.date	2004-10-04T14:49:37Z
dc.date	2004-10-04T14:49:37Z
dc.date	1978-05-01
dc.date.accessioned	2013-10-09T02:44:46Z
dc.date.available	2013-10-09T02:44:46Z
dc.date.issued	2013-10-09
dc.identifier	AIM-475
dc.identifier	http://hdl.handle.net/1721.1/6297
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	Most real world domains differ from the micro-worlds traditionally used in A.I. in that they have an incomplete factual database which changes over time. Understanding in these domains can be thought of as the generation of plausible inferences which are able to use the facts available, and respond to changes in them. A traditional rule interpreter such as Planner can be extended to construct plausible inferences in these domains by A) allowing assumptions to be made in applying rules, resulting in simplifications of rules which can be used in an incomplete database; B) monitoring the antecedents and consequents of a rule so that inferences can be maintained over a changing database. The resulting chains of inference can provide a dynamic description of an event. This allows general reasoning processes to be used to understand in domains for which large numbers of Schema-like templates have been proposed as the best model.
dc.format	7684915 bytes
dc.format	5612742 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-475
dc.title	Understanding in Incomplete Worlds

Files in this item

Files	Size	Format	View
There are no files associated with this item.