Abstract
Setting the right price at a gas station is a complex task involving numerous parameters. By using a hybrid agent architecture based on BDI and ANN we can model a gas station agent that can learn to model its consumers. The gas station agent can then use the learning from it’s consumer behavior to detect anomalies in the environment and autonomously set its own price to influence the consumer and thereby optimize e.g. gross margin without sacrificing volume.
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A2I Systems. Pricecast fuel. http://www.a2isystems.com/pricecast.html. Accessed 12 Feb 2016
A2I Systems. Pricecast fuel: Reference story. http://www.a2isystems.com/files/pdf/PriceCast%20Fuel%20Case%20Story%20(’15).pdf. Accessed 12 Feb 2016
Angwin, J., Mattioli, D.: Coming soon: toilet paper priced like airline tickets. http://on.wsj.com/1QfNTMe. Accessed 12 Feb 2016
Bratman, M.E., Israel, D.J., Pollack, M.E.: Plans and resource-bounded practical reasoning. Comput. Intell. 4, 349–355 (1988)
Derakhshan, A., Hammer, F., Lund, H., Demazeau, Y.: Mapping children and playgrounds into multi-agent systems (2006)
Lane, T., Brodley, C.E.: An application of machine learning to anomaly detection. In: Proceedings of the 20th National Information Systems Security Conference, pp. 366–380 (1997)
Noel, M.D.: Edgeworth price cycles. In: Durlauf, S.N., Blume, L.E. (eds.) The New Palgrave Dictionary of Economics. Palgrave Macmillan, Basingstoke (2011)
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© 2016 Springer International Publishing Switzerland
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Derakhshan, A., Hammer, F., Demazeau, Y. (2016). PriceCast Fuel: Agent Based Fuel Pricing. In: Demazeau, Y., Ito, T., Bajo, J., Escalona, M. (eds) Advances in Practical Applications of Scalable Multi-agent Systems. The PAAMS Collection. PAAMS 2016. Lecture Notes in Computer Science(), vol 9662. Springer, Cham. https://doi.org/10.1007/978-3-319-39324-7_23
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DOI: https://doi.org/10.1007/978-3-319-39324-7_23
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Publisher Name: Springer, Cham
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