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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Approximating Multi-attribute Resource Allocations Using GAI Utility Functions

verfasst von : Charles Harold, Mohan Baruwal Chhetri, Ryszard Kowalczyk

Erschienen in: Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection

Verlag: Springer International Publishing

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Abstract

The design of Multi-Attribute Double-Sided Auctions (MADSA) is an important problem being examined in a variety of domains. Despite significant efforts, an ideal compromise between expressiveness of preference representation and the tractability of MADSA mechanisms is still subject to much debate. In this paper, we propose a MADSA mechanism whereby bids are placed in the form of Generalised Additively Independent-Decomposable (GAI-D) utility functions. We show that by applying a set of constraints on the composition of these functions a relaxation of the Kalai bargaining solution becomes tractable for large double-sided markets. Experimental results show that the proposed mechanism provides efficient results when compared to the well known k-priced greedy market mechanism.

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Vorheriges Kapitel A New Deep Hierarchical Neural Network Applied in Human Activity Recognition (HAR) Using Wearable Sensors
Nächstes Kapitel Multiagent Reinforcement Learning Applied to Traffic Light Signal Control
Fußnoten
1
When arbitrating bids k-pricing allocates the bid package defined by the bidder (assuming the vendor has sufficient volume of goods) at a price (P) between the two bids. Specifically, the excess value (difference between buyer offer \(B_o\) and vendor reserve \(V_r\)) is allocated between bidder and vendor according to some ratio k, that is \(P = k \cdot (B_o - V_r) + V_r\) [24].
 
2
We only discuss non-continuous or call-market mechanisms in this paper.
 
3
Myerson and Satterthwaite [17] define an ex-post efficient market as one that awards the good to the bidder with a higher valuation (be that seller or buyer).
 
4
In our testing framework a bid also entails a start time for the parking allocation, these are used to filter possible pairings, this puts limitations on the efficiency of scheduling, a complex issue discussed in detail by Fujiwara et al. [9] and others.
 
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Metadaten
Titel
Approximating Multi-attribute Resource Allocations Using GAI Utility Functions
verfasst von
Charles Harold
Mohan Baruwal Chhetri
Ryszard Kowalczyk
Copyright-Jahr
2019
Buch
Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection

Print ISBN: 978-3-030-24208-4

Electronic ISBN: 978-3-030-24209-1

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-24209-1_9

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