The University of Southampton
University of Southampton Institutional Repository

Bidding Agents for Multiple Heterogeneous Online Auctions

Bidding Agents for Multiple Heterogeneous Online Auctions
Bidding Agents for Multiple Heterogeneous Online Auctions
Due to the proliferation of online auctions, there is an increasing need to monitor and bid in multiple auctions in order to procure the best deal for the desired good. To this end, this thesis reports on the development of a heuristic decision making framework that an autonomous agent can exploit to tackle the problem of bidding across multiple auctions with varying start and end times and with varying protocols (including English, Dutch and Vickrey). The framework is flexible, configurable, and enables the agent to adopt varying tactics and strategies that attempt to ensure that the desired item is delivered in a manner consistent with the user's preferences. Given this large space of possibilities, a genetic algorithm is employed to search (offline) for effective strategies in common classes of environment. The strategies that emerge from this evolution are then codified into the agent's reasoning behaviour so that it can select the most appropriate strategy to employ in its prevailing circumstances. The proposed framework has been implemented in a simulated marketplace environment and its effectiveness has been empirically demonstrated.
bidding agent multiple auctions decision-making framework genetic algorithms
Anthony, P
a711e531-d9f7-4aee-a740-94ab9d3c4a54
Anthony, P
a711e531-d9f7-4aee-a740-94ab9d3c4a54

Anthony, P (2003) Bidding Agents for Multiple Heterogeneous Online Auctions. University of Southampton, Electronics and Computer Science, Doctoral Thesis.

Record type: Thesis (Doctoral)

Abstract

Due to the proliferation of online auctions, there is an increasing need to monitor and bid in multiple auctions in order to procure the best deal for the desired good. To this end, this thesis reports on the development of a heuristic decision making framework that an autonomous agent can exploit to tackle the problem of bidding across multiple auctions with varying start and end times and with varying protocols (including English, Dutch and Vickrey). The framework is flexible, configurable, and enables the agent to adopt varying tactics and strategies that attempt to ensure that the desired item is delivered in a manner consistent with the user's preferences. Given this large space of possibilities, a genetic algorithm is employed to search (offline) for effective strategies in common classes of environment. The strategies that emerge from this evolution are then codified into the agent's reasoning behaviour so that it can select the most appropriate strategy to employ in its prevailing circumstances. The proposed framework has been implemented in a simulated marketplace environment and its effectiveness has been empirically demonstrated.

Text
PatThesis.pdf - Other
Restricted to Registered users only
Download (1MB)

More information

Published date: June 2003
Keywords: bidding agent multiple auctions decision-making framework genetic algorithms
Organisations: University of Southampton, Electronics & Computer Science

Identifiers

Local EPrints ID: 257838
URI: http://eprints.soton.ac.uk/id/eprint/257838
PURE UUID: 9afefb66-cd6c-421d-a748-42b2163d8834

Catalogue record

Date deposited: 25 Jun 2003
Last modified: 10 Dec 2021 20:53

Export record

Contributors

Author: P Anthony

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×