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Pareto optimal allocation under compact uncertain preferences

Pareto optimal allocation under compact uncertain preferences
Pareto optimal allocation under compact uncertain preferences
The assignment problem is one of the most well-studied settings in multi-agent resource allocation. Aziz, de Haan, and Rastegari (2017) considered this problem with the additional feature that agents preferences involve uncertainty. In particular, they considered two uncertainty models neither of which is necessarily compact. In this paper, we focus on three uncertain preferences models whose size is polynomial in the number of agents and items. We consider several interesting computational questions with regard to Pareto optimal assignments. We also present some general characterization and algorithmic results that apply to large classes of uncertainty models.
Aziz, Haris
99c295f2-f10c-4e8e-b48c-3f4048a075f3
Biro, Peter
18b4be5b-765a-49ab-9e13-fa0ae49bd564
de Haan, Ronald
2d29a3e0-f335-4cdf-a62b-c1c293283839
Rastegari, Baharak
6ba9e93c-53ba-4090-8f77-c1cb1568d7d1
Aziz, Haris
99c295f2-f10c-4e8e-b48c-3f4048a075f3
Biro, Peter
18b4be5b-765a-49ab-9e13-fa0ae49bd564
de Haan, Ronald
2d29a3e0-f335-4cdf-a62b-c1c293283839
Rastegari, Baharak
6ba9e93c-53ba-4090-8f77-c1cb1568d7d1

Aziz, Haris, Biro, Peter, de Haan, Ronald and Rastegari, Baharak (2018) Pareto optimal allocation under compact uncertain preferences. Thirty Third AAAI Conference on Artificial Intelligence, Honolulu, United States. 27 Jan - 01 Feb 2019. (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

The assignment problem is one of the most well-studied settings in multi-agent resource allocation. Aziz, de Haan, and Rastegari (2017) considered this problem with the additional feature that agents preferences involve uncertainty. In particular, they considered two uncertainty models neither of which is necessarily compact. In this paper, we focus on three uncertain preferences models whose size is polynomial in the number of agents and items. We consider several interesting computational questions with regard to Pareto optimal assignments. We also present some general characterization and algorithmic results that apply to large classes of uncertainty models.

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More information

Accepted/In Press date: 31 October 2018
Venue - Dates: Thirty Third AAAI Conference on Artificial Intelligence, Honolulu, United States, 2019-01-27 - 2019-02-01

Identifiers

Local EPrints ID: 425734
URI: http://eprints.soton.ac.uk/id/eprint/425734
PURE UUID: 40a1fd13-156c-44d0-8424-e8fc9a6d5e65
ORCID for Baharak Rastegari: ORCID iD orcid.org/0000-0002-0985-573X

Catalogue record

Date deposited: 02 Nov 2018 17:30
Last modified: 14 Mar 2019 01:20

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Contributors

Author: Haris Aziz
Author: Peter Biro
Author: Ronald de Haan
Author: Baharak Rastegari ORCID iD

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