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Rational bidding using reinforcement learning: an application in automated resource allocation
Number of Authors: 4
2008 (English)Conference paper (Refereed)
Abstract [en]

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized.

Place, publisher, year, edition, pages
2008, 1. , 16 p.
Keyword [en]
Bid Generation, Reinforcement learning, Service Provisioning and Usage, Grid Computing
National Category
Computer and Information Science
URN: urn:nbn:se:ri:diva-15141DOI: 10.1007/978-3-540-85485-2_6OAI: diva2:1036435
The 5th International Workshop on Grid Economics and Business Models (Gecon 2008)
Available from: 2016-10-13 Created: 2016-10-13

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ReferencesLink to record
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