Search Swinburne Research Bank
This object has not yet been indexed by the background indexing service.
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/47684
|Download PDF (Published version) (Adobe Acrobat PDF, -1 bytes)|
- Learning the IPA market with individual and social rewards
- Gomes, Eduardo Rodrigues; Kowalczyk, Ryszard
- 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
- 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
- Publisher URL
- 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.