Agents preferences in decentralized task allocation

Mark Hoogendoorn; Maria L. Gini

doi:10.3233/978-1-58603-891-5-398

Agents preferences in decentralized task allocation

Mark Hoogendoorn, Maria L. Gini

Computer Science and Engineering

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

Abstract

The ability to express preferences for specific tasks in multi-agent auctions is an important element for potential users who are considering to use such auctioning systems. This paper presents an approach to make such preferences explicit and to use these preferences in bids for reverse combinatorial auctions. Three different types of preference are considered: (1) preferences for particular durations of tasks, (2) preferences for certain time points, and (3) preferences for specific types of tasks. We study empirically the tradeoffs between the quality of the solutions obtained and the use of preferences in the bidding process, focusing on effects such as increased execution time. We use both synthetic data as well as real data from a logistics company.

Original language	English (US)
Title of host publication	Frontiers in Artificial Intelligence and Applications
Publisher	IOS Press
Pages	398-402
Number of pages	5
ISBN (Print)	978158603891
DOIs	https://doi.org/10.3233/978-1-58603-891-5-398
State	Published - Jun 2008
Event	18th European Conference on Artificial Intelligence, ECAI 2008 - Patras, Greece Duration: Jul 21 2008 → Jul 25 2008

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	178
ISSN (Print)	0922-6389

Conference

Conference	18th European Conference on Artificial Intelligence, ECAI 2008
Country/Territory	Greece
City	Patras
Period	7/21/08 → 7/25/08

Bibliographical note

Funding Information:
Acknowledgments: Partial support is gratefully acknowledged from NSF under grant IIS-0414466.

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10.3233/978-1-58603-891-5-398

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title = "Agents preferences in decentralized task allocation",

abstract = "The ability to express preferences for specific tasks in multi-agent auctions is an important element for potential users who are considering to use such auctioning systems. This paper presents an approach to make such preferences explicit and to use these preferences in bids for reverse combinatorial auctions. Three different types of preference are considered: (1) preferences for particular durations of tasks, (2) preferences for certain time points, and (3) preferences for specific types of tasks. We study empirically the tradeoffs between the quality of the solutions obtained and the use of preferences in the bidding process, focusing on effects such as increased execution time. We use both synthetic data as well as real data from a logistics company.",

author = "Mark Hoogendoorn and Gini, {Maria L.}",

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AU - Gini, Maria L.

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N2 - The ability to express preferences for specific tasks in multi-agent auctions is an important element for potential users who are considering to use such auctioning systems. This paper presents an approach to make such preferences explicit and to use these preferences in bids for reverse combinatorial auctions. Three different types of preference are considered: (1) preferences for particular durations of tasks, (2) preferences for certain time points, and (3) preferences for specific types of tasks. We study empirically the tradeoffs between the quality of the solutions obtained and the use of preferences in the bidding process, focusing on effects such as increased execution time. We use both synthetic data as well as real data from a logistics company.

AB - The ability to express preferences for specific tasks in multi-agent auctions is an important element for potential users who are considering to use such auctioning systems. This paper presents an approach to make such preferences explicit and to use these preferences in bids for reverse combinatorial auctions. Three different types of preference are considered: (1) preferences for particular durations of tasks, (2) preferences for certain time points, and (3) preferences for specific types of tasks. We study empirically the tradeoffs between the quality of the solutions obtained and the use of preferences in the bidding process, focusing on effects such as increased execution time. We use both synthetic data as well as real data from a logistics company.

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AN - SCOPUS:85051919264

SN - 978158603891

T3 - Frontiers in Artificial Intelligence and Applications

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BT - Frontiers in Artificial Intelligence and Applications

PB - IOS Press

T2 - 18th European Conference on Artificial Intelligence, ECAI 2008

Y2 - 21 July 2008 through 25 July 2008

ER -

Agents preferences in decentralized task allocation

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