@inbook{2f794788922b4a8aade8b0c5b5132ae8,
title = "An adaptive agent model for generator company bidding in the UK power pool",
abstract = "This paper describes an autonomous adaptive agent model of the UK market in electricity, where the agents represent electricity generating companies. We briefly describe the UK market in electricity generation, then detail the simplifications we have made. Our current model consists of a single adaptive agent bidding against several non-adaptive agents. The adaptive agent uses a hierarchical agent structure with two Learning Classifier Systems to evolve market bidding rules to meet two objectives. We detail how the agent interacts with its environment, the particular problems this environment presents to the agent and the agent and classifier architectures we used in our experiments. We present the results and conclude that using our structure can improve performance.",
author = "Bagnall, {A. J.} and Smith, {G. D.}",
year = "2000",
doi = "10.1007/10721187_14",
language = "English",
isbn = "978-3-540-67846-5",
volume = "1829",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "191--203",
editor = "Cyril Fonlupt and Jin-Kao Hao and Evelyne Lutton and Marc Schoenauer and Edmund Ronald",
booktitle = "Artificial Evolution",
address = "Germany",
}