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dc.contributor.authorShenoy, Prakash P.
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.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.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.oapolicyThis item does not meet KU Open Access policy criteria.

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