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Title

Learning the IPA market with individual and social rewards

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. different price or resource levels. One of the alternatives to address this limitation is to describe the agents' preferences using utility functions. In such a scenario, however, there is no unique mapping between the utility functions and a demand function. Gomes & Kowalczyk [10, 9] proposed the use of Reinforcement Learning to let the agents learn the demand functions given the utility functions. Their approach is based on the individual utilities of the agents at the end of the allocation. In this paper, we extend such a work by applying a new reward function, based on the social welfare of the allocation, and by considering more clients in the market. The learning process and the behavior of the agents using both reward functions are investigated through experiments and the results compared.

Publication type

Conference paper

Research centre

Swinburne University of Technology. Faculty of Information and Communication Technologies

Source

Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT 2007, Silicon Valley, USA, 02-05 November 2007 / Tsau Young (T.Y.) Lin, Jeffrey M. Bradshaw, Matthias Klusch, Chengqi Zhang, Andrei Broder and Howard Ho (eds.), pp. 328-334

Publication year

2007

Keyword(s)

Distributed allocation;  Functions;  Learning systems;  Resource allocation;  Resource levels;  Social aspects;  Social rewards;  Utility functions

Publisher

IEEE

ISBN

9780769530277, 0769530273

Publisher URL

http://dx.doi.org/10.1109/IAT.2007.49

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