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dc.contributor.authorShenoy, Prakash P.
dc.date.accessioned2004-12-20T15:21:09Z
dc.date.available2004-12-20T15:21:09Z
dc.date.issued1995-08
dc.identifier.citationShenoy, P. P., "A New Pruning Method for Solving Decision Trees and Game Trees," in P. Besnard and S. Hanks (eds.), Uncertainty in Artificial Intelligence, Vol. 11, 1995, pp. 482--490, Morgan Kaufmann, San Francisco, CA.
dc.identifier.isbn1-55860-385-9
dc.identifier.urihttp://hdl.handle.net/1808/179
dc.description.abstractThe main goal of this paper is to describe a newpruning method for solving decision trees and game trees. The pruning method for decision trees suggests a slight variant of decision trees that we call scenario trees. In scenario trees, we do not need a conditional probability for each edge emanating from a chance node. Instead, we require a joint probability for each path from the root node to a leaf node. We compare the pruning method to the traditional rollback method for decision trees and game trees. For problems that require Bayesian revision of probabilities, a scenario tree representation with the pruning method is more efficient than a decision tree representation with the rollback method. For game trees, the pruning method is more efficient than the rollback method.
dc.format.extent145991 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherMorgan Kaufmann Publishers
dc.subjectDecision trees
dc.subjectGame trees
dc.subjectRoll-back method
dc.subjectScenario trees
dc.titleA New Pruning Method for Solving Decision Trees and Game Trees
dc.typeBook chapter
kusw.oastatusna
dc.identifier.orcidhttps://orcid.org/0000-0002-8425-896X
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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