Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities

Daniel Kikuti; Fabio Gagliardi Cozman; Cassio P. de Campos

Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities

Daniel Kikuti, Fabio Gagliardi Cozman, Cassio P. de Campos

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

Partially ordered preferences generally lead to choices that do not abide by standard expected utility guidelines; often such preferences are revealed by imprecision in probability values. We investigate five criteria for strategy selection in decision trees with imprecision in probabilities: “extensive” Γ-maximin and Γ-maximax, interval dominance, maximality and E-admissibility. We present algorithms that generate strategies for all these criteria; our main contribution is an algorithm for Eadmissibility that runs over admissible strategies rather than over sets of probability distributions.

Original language	English
Title of host publication	Proceedings of the IJCAI Workshop about Advances on Preference Handling
Publisher	International Joint Conferences on Artificial Intelligence (IJCAI)
Pages	118-123
Number of pages	6
Publication status	Published - 2005
Externally published	Yes
Event	IJCAI-05 Multidisciplinary Workshop on Advances in Preference Handling - Edinburgh, United Kingdom Duration: 31 Jul 2005 → 1 Aug 2005

Workshop

Workshop	IJCAI-05 Multidisciplinary Workshop on Advances in Preference Handling
Country/Territory	United Kingdom
City	Edinburgh
Period	31/07/05 → 1/08/05

Cite this

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title = "Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities",

abstract = "Partially ordered preferences generally lead to choices that do not abide by standard expected utility guidelines; often such preferences are revealed by imprecision in probability values. We investigate five criteria for strategy selection in decision trees with imprecision in probabilities: “extensive” Γ-maximin and Γ-maximax, interval dominance, maximality and E-admissibility. We present algorithms that generate strategies for all these criteria; our main contribution is an algorithm for Eadmissibility that runs over admissible strategies rather than over sets of probability distributions.",

author = "Daniel Kikuti and Cozman, \{Fabio Gagliardi\} and \{de Campos\}, \{Cassio P.\}",

year = "2005",

language = "English",

pages = "118--123",

booktitle = "Proceedings of the IJCAI Workshop about Advances on Preference Handling",

publisher = "International Joint Conferences on Artificial Intelligence (IJCAI)",

note = "IJCAI-05 Multidisciplinary Workshop on Advances in Preference Handling ; Conference date: 31-07-2005 Through 01-08-2005",

}

Kikuti, D, Cozman, FG & de Campos, CP 2005, Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities. in Proceedings of the IJCAI Workshop about Advances on Preference Handling. International Joint Conferences on Artificial Intelligence (IJCAI), pp. 118-123, IJCAI-05 Multidisciplinary Workshop on Advances in Preference Handling, Edinburgh, United Kingdom, 31/07/05.

Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities. / Kikuti, Daniel; Cozman, Fabio Gagliardi; de Campos, Cassio P.
Proceedings of the IJCAI Workshop about Advances on Preference Handling. International Joint Conferences on Artificial Intelligence (IJCAI), 2005. p. 118-123.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities

AU - Kikuti, Daniel

AU - Cozman, Fabio Gagliardi

AU - de Campos, Cassio P.

PY - 2005

Y1 - 2005

N2 - Partially ordered preferences generally lead to choices that do not abide by standard expected utility guidelines; often such preferences are revealed by imprecision in probability values. We investigate five criteria for strategy selection in decision trees with imprecision in probabilities: “extensive” Γ-maximin and Γ-maximax, interval dominance, maximality and E-admissibility. We present algorithms that generate strategies for all these criteria; our main contribution is an algorithm for Eadmissibility that runs over admissible strategies rather than over sets of probability distributions.

AB - Partially ordered preferences generally lead to choices that do not abide by standard expected utility guidelines; often such preferences are revealed by imprecision in probability values. We investigate five criteria for strategy selection in decision trees with imprecision in probabilities: “extensive” Γ-maximin and Γ-maximax, interval dominance, maximality and E-admissibility. We present algorithms that generate strategies for all these criteria; our main contribution is an algorithm for Eadmissibility that runs over admissible strategies rather than over sets of probability distributions.

M3 - Chapter

SP - 118

EP - 123

BT - Proceedings of the IJCAI Workshop about Advances on Preference Handling

PB - International Joint Conferences on Artificial Intelligence (IJCAI)

T2 - IJCAI-05 Multidisciplinary Workshop on Advances in Preference Handling

Y2 - 31 July 2005 through 1 August 2005

ER -

Partially ordered preferences in decision trees: computing strategies with imprecision in probabilities

Abstract

Workshop

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