Computing optimal subsets

Maxim Binshtok; Ronen I. Brafman; Solomon E. Shimony; Ajay Martin; Craig Boutilier

Computing optimal subsets

Maxim Binshtok
, Ronen I. Brafman
, Solomon E. Shimony
, Ajay Martin
, Craig Boutilier

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

2 Scopus citations

Abstract

Various tasks in decision making and decision support require selecting a preferred subset of items from a given set of feasible items. Recent work in this area considered methods for specifying such preferences based on the attribute values of individual elements within the set. Of these, the approach of (Brafman et al. 2006) appears to be the most general. In this paper, we consider the problem of computing an optimal subset given such a specification. The problem is shown to be NP-hard in the general case, necessitating heuristic search methods. We consider two algorithm classes for this problem: direct set construction, and implicit enumeration as solutions to appropriate CSPs. New algorithms are presented in each class and compared empirically against previous results.

Original language	English
Title of host publication	Preference Handling for Artificial Intelligence - Papers from the 2007 AAAI Workshop, Technical Report
Pages	23-30
Number of pages	8
State	Published - 1 Dec 2007
Event	2007 AAAI Workshop - Vancouver, BC, Canada Duration: 22 Jul 2007 → 22 Jul 2007

Publication series

Name	AAAI Workshop - Technical Report
Volume	WS-07-10

Conference

Conference	2007 AAAI Workshop
Country/Territory	Canada
City	Vancouver, BC
Period	22/07/07 → 22/07/07

ASJC Scopus subject areas

General Engineering

Cite this

@inproceedings{72ab7c5f9f744d34a587c71f67731a6e,

title = "Computing optimal subsets",

abstract = "Various tasks in decision making and decision support require selecting a preferred subset of items from a given set of feasible items. Recent work in this area considered methods for specifying such preferences based on the attribute values of individual elements within the set. Of these, the approach of (Brafman et al. 2006) appears to be the most general. In this paper, we consider the problem of computing an optimal subset given such a specification. The problem is shown to be NP-hard in the general case, necessitating heuristic search methods. We consider two algorithm classes for this problem: direct set construction, and implicit enumeration as solutions to appropriate CSPs. New algorithms are presented in each class and compared empirically against previous results.",

author = "Maxim Binshtok and Brafman, \{Ronen I.\} and Shimony, \{Solomon E.\} and Ajay Martin and Craig Boutilier",

year = "2007",

month = dec,

day = "1",

language = "English",

isbn = "9781577353379",

series = "AAAI Workshop - Technical Report",

pages = "23--30",

booktitle = "Preference Handling for Artificial Intelligence - Papers from the 2007 AAAI Workshop, Technical Report",

note = "2007 AAAI Workshop ; Conference date: 22-07-2007 Through 22-07-2007",

}

TY - GEN

T1 - Computing optimal subsets

AU - Binshtok, Maxim

AU - Brafman, Ronen I.

AU - Shimony, Solomon E.

AU - Martin, Ajay

AU - Boutilier, Craig

PY - 2007/12/1

Y1 - 2007/12/1

N2 - Various tasks in decision making and decision support require selecting a preferred subset of items from a given set of feasible items. Recent work in this area considered methods for specifying such preferences based on the attribute values of individual elements within the set. Of these, the approach of (Brafman et al. 2006) appears to be the most general. In this paper, we consider the problem of computing an optimal subset given such a specification. The problem is shown to be NP-hard in the general case, necessitating heuristic search methods. We consider two algorithm classes for this problem: direct set construction, and implicit enumeration as solutions to appropriate CSPs. New algorithms are presented in each class and compared empirically against previous results.

AB - Various tasks in decision making and decision support require selecting a preferred subset of items from a given set of feasible items. Recent work in this area considered methods for specifying such preferences based on the attribute values of individual elements within the set. Of these, the approach of (Brafman et al. 2006) appears to be the most general. In this paper, we consider the problem of computing an optimal subset given such a specification. The problem is shown to be NP-hard in the general case, necessitating heuristic search methods. We consider two algorithm classes for this problem: direct set construction, and implicit enumeration as solutions to appropriate CSPs. New algorithms are presented in each class and compared empirically against previous results.

UR - https://www.scopus.com/pages/publications/51849157275

M3 - Conference contribution

AN - SCOPUS:51849157275

SN - 9781577353379

T3 - AAAI Workshop - Technical Report

SP - 23

EP - 30

BT - Preference Handling for Artificial Intelligence - Papers from the 2007 AAAI Workshop, Technical Report

T2 - 2007 AAAI Workshop

Y2 - 22 July 2007 through 22 July 2007

ER -

Computing optimal subsets

Abstract

Publication series

Conference

ASJC Scopus subject areas

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