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Title

Learning in market-based resource allocation

Author(s)

Gomes, Eduardo Rodrigues;  Kowalczyk, Ryszard

Abstract

Market-based mechanisms offer a promising approach for distributed allocation of resources without centralized control. One of those mechanisms is the Iterative Price Adjustment (IPA). Under standard assumptions, the IPA uses demand functions that do not allow the agents to have preferences over some attributes of the allocation, e.g. the price of the resources. To address this limitation, we study the case where the agents' preferences are described by utility functions. In such a scenario, however, there is no unique mapping between the utility functions and a demand function. If made 'by hand', this task can be very subjective and time consuming. Thus, we propose and investigate the use of Reinforcement Learning to let the agents learn the best demand functions given their utility functions. The approach is evaluated in two scenarios.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Faculty of Information and Communication Technologies

Source

Proceedings of the 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS), held jointly with the 1st IEEE/ACIS International Workshop on e-Activity (IWEA 2007), Melbourne, Victoria, Australia, 11-13 July 2007, pp. 475-482

Publication year

2007

Keyword(s)

IPA;  Iterative Price Adjustment;  Market-based mechanisms;  Reinforcement learning

Publisher

IEEE

ISBN

9780769528410, 0769528414

Publisher URL

http://dx.doi.org/10.1109/ICIS.2007.126

Copyright

Copyright © 2007 IEEE. Published version of the paper reproduced here in accordance with the copyright policy of the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Full text

Peer reviewed