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The electricity market within the United Kingdom has undergone a period of rapid and sustained development since the introduction of the New Electricity Trading Arrangements (NETA) in 2001. Competition within the market has greatly intensified placing significant downward pressure upon the wholesale price of electricity. In addition, the dynamic nature of the NETA together with the development of distributed and embedded generation technologies will lead to a significant increase in complexity, both within the physical and market domains. We propose that fuzzy cognitive maps (FCM) can act as powerful inference engine with in autonomous adaptive agent (AI-Agent) based architecture to model such systems. This paper will examine the generic structure of the FCMs, their construction, and the learning algorithms to allow them to adapt to the dynamic market based environment. The concept of temporal delay within the FCMs to describe the inertia that exists in real time systems shall also be discussed.