Showing papers by "Anthony J. Bagnall published in 2000"

A multi-adaptive agent model of generator bidding in the UK market in electricity

Abstract: A model of the UK market in electricity combining key factors influencing generator bidding is proposed and a hierarchical multi-objective adaptive agent architecture using case based reasoning and learning classifier systems is described. Experimentation shows that the adaptive agents learn bidding strategies that have been observed in the real world, and that in some market scenarios the agents appear to be learning the benefits of cooperating to receive increased long term rewards. The potential of the adaptive agent model is illustrated by experimentation with an alternative market structure.

Game playing with autonomous adaptive agents in a simplified economic model of the UK market in electricity generation

Abstract: We describe a simplified model of the UK market in electricity where autonomous adaptive agents representing electricity generation companies compete by bidding for the right to generate in a series of noncooperative games simulating scenarios seen in the real world market. We investigate the effects on agent behaviour of alterations of the settlement method and examine under what conditions cooperation to receive increased payments can emerge between the agents.

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.

A Multi-Adaptive Agent Model for Generator Company Bidding in the UK Market in Electricity

Abstract: A model of the UK market in electricity combining key factors influencing generator bidding is proposed and a hierarchical multi-objective adaptive agent architecture using case based reasoning and learning classifier systems is described. Experimentation shows that the adaptive agents learn bidding strategies that have been observed in the real world, and that in some market scenarios the agents appear to be learning the benefits of cooperating to receive increased long term rewards. The potential of the adaptive agent model is illustrated by experimentation with an alternative market structure.