Adaptive bidding strategies in agent-based combinatorial auctions.

Sui, Xin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 91-97).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- MAS-Based Resource Allocation Problems --- p.1Chapter 1.2 --- Combinatorial Auction As a Solution --- p.4Chapter 1.3 --- Strategy Issues --- p.5Chapter 1.4 --- Structure of This Work --- p.7Chapter 2 --- Combinatorial Auctions and Bidding Strategies --- p.9Chapter 2.1 --- Combinatorial Auctions for Resource Allocation --- p.9Chapter 2.2 --- Bidding Strategies in Combinatorial Auctions --- p.11Chapter 2.2.1 --- Berhault's Strategies --- p.11Chapter 2.2.2 --- An's Strategies --- p.13Chapter 2.2.3 --- Schwind´ةs Strategies --- p.15Chapter 2.2.4 --- Wilenius´ةs Strategy --- p.17Chapter 2.2.5 --- Overview of Previous Strategies --- p.18Chapter 3 --- An Adaptive Bidding Strategy in Static Markets --- p.21Chapter 3.1 --- Basic Concepts --- p.22Chapter 3.2 --- The Core Algorithm --- p.24Chapter 3.3 --- Experimental Evaluation --- p.31Chapter 3.3.1 --- Experiment Setup --- p.32Chapter 3.3.2 --- Experiment Results and Analysis --- p.33Chapter 4 --- An Adaptive Bidding Strategy in Dynamic Markets --- p.38Chapter 4.1 --- Basic Concepts --- p.39Chapter 4.2 --- The Core Algorithm --- p.42Chapter 4.3 --- Experimental Evaluation --- p.48Chapter 4.3.1 --- Experiment Setup --- p.49Chapter 4.3.2 --- Experiment Results and Analysis --- p.51Chapter 5 --- A Q-Learning Based Adaptive Bidding Strategy in Static Mar-kets --- p.59Chapter 5.1 --- An Overview of Q-Learning --- p.60Chapter 5.2 --- Basic Concepts --- p.63Chapter 5.3 --- The Core Algorithm --- p.65Chapter 5.4 --- Experimental Evaluation --- p.70Chapter 5.4.1 --- Experiment Setup --- p.70Chapter 5.4.2 --- Experiment Results and Analysis --- p.72Chapter 6 --- Discussion --- p.82Chapter 6.1 --- Applicability of the Adaptive Strategies --- p.82Chapter 6.2 --- Generalization of the Adaptive Strategies --- p.83Chapter 7 --- Conclusion and Future Work --- p.86Chapter 7.1 --- Conclusion --- p.86Chapter 7.2 --- Future Work --- p.88Bibliography --- p.9

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services 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 service provisioning and usage of Grid services, 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 bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

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Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1

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A Market-based Approach to Multi-factory Scheduling

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This dissertation contributes to the research on Computational Mechanism Design by providing novel theoretical and software models - a novel bidding strategy called Q-Strategy, which automates bidding processes in imperfect information markets, a software framework for realizing agents and bidding strategies called BidGenerator and a communication protocol called MX/CS, for expressing and exchanging economic and technical information in a market-based scheduling system

Combinatorial Auction-based Mechanisms for Composite Web Service Selection

Composite service selection presents the opportunity for the rapid development of complex applications using existing web services. It refers to the problem of selecting a set of web services from a large pool of available candidates to logically compose them to achieve value-added composite services. The aim of service selection is to choose the best set of services based on the functional and non-functional (quality related) requirements of a composite service requester. The current service selection approaches mostly assume that web services are offered as single independent entities; there is no possibility for bundling. Moreover, the current research has mainly focused on solving the problem for a single composite service. There is a limited research to date on how the presence of multiple requests for composite services affects the performance of service selection approaches. Addressing these two aspects can significantly enhance the application of composite service selection approaches in the real-world. We develop new approaches for the composite web service selection problem by addressing both the bundling and multiple requests issues. In particular, we propose two mechanisms based on combinatorial auction models, where the provisioning of multiple services are auctioned simultaneously and service providers can bid to offer combinations of web services. We mapped these mechanisms to Integer Linear Programing models and conducted extensive simulations to evaluate them. The results of our experimentation show that bundling can lead to cost reductions compared to when services are offered independently. Moreover, the simultaneous consideration of a set of requests enhances the success rate of the mechanism in allocating services to requests. By considering all composite service requests at the same time, the mechanism achieves more homogenous prices which can be a determining factor for the service requester in choosing the best composite service selection mechanism to deploy

Theoretical and Computational Basis for CATNETS - Annual Report Year 3

In this document the developments in defining the computational and theoretical framework for economical resource allocation are described. Accordingly the formal specification of the market mechanisms, bidding strategies of the involved agents and the integration of the market mechanisms into the simulator were refined. --Grid Computing