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http://hdl.handle.net/1808/179
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| DC Field | Value | Language |
|---|
| dc.contributor.author | Shenoy, Prakash P. | - |
| dc.date.accessioned | 2004-12-20T15:21:09Z | - |
| dc.date.available | 2004-12-20T15:21:09Z | - |
| dc.date.issued | 1995-08 | - |
| dc.identifier.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. | en |
| dc.identifier.isbn | 1-55860-385-9 | - |
| dc.identifier.uri | http://hdl.handle.net/1808/179 | - |
| dc.description.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. | en |
| dc.format.extent | 145991 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.publisher | Morgan Kaufmann Publishers | en |
| dc.subject | Decision trees | en |
| dc.subject | Game Trees | en |
| dc.subject | Roll-back method | en |
| dc.subject | Scenario trees | en |
| dc.title | A New Pruning Method for Solving Decision Trees and Game Trees | en |
| dc.type | Book chapter | en |
| Appears in Collections: | School of Business Articles
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