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    A New Pruning Method for Solving Decision Trees and Game Trees

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    UAI95.pdf (142.5Kb)
    Issue Date
    1995-08
    Author
    Shenoy, Prakash P.
    Publisher
    Morgan Kaufmann Publishers
    Format
    145991 bytes
    Type
    Book chapter
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    Abstract
    The 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.
    URI
    http://hdl.handle.net/1808/179
    ISBN
    1-55860-385-9
    Collections
    • Distinguished Professors Scholarly Works [918]
    • School of Business Scholarly Works [212]
    Citation
    Shenoy, 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.

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    KU Libraries
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    785-864-8983

    KU Libraries
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    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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