Realisation of Fuzzy Cognitive Agents in the electrical trading domain

Conceptually the fuzzy cognitive agent (FCA) is the novel integration of fuzzy cognitive maps (FCM) and adaptive intelligent agent (AI-Agent) paradigms. FCAs fundamentally differ from traditional rule based AI-Agents as their inference is derived by a unique FCM that encapsulates the agent's beliefs and desires. The benefits of this architecture are two fold; firstly the knowledge elicitation process to realise the inference engine of FCAs is enhanced by the visuality offered by FCMs; secondly the AI-Agent paradigm permits the fragmentation of the homogeneous domain FCM into functionally specific and discrete components. Discrete FCAs can then interact using standard AI-Agent protocols. Further, as a consequence of the distributed characteristic of the paradigm, naturally fragmented domains characterised by partial data availability can now be accurately captured in contrast to traditional FCMs that act homogeneously towards a single goal. As a first step, this article demonstrates the potential of the paradigm within the electricity market domain by integration of FCMs supported on Matlab Simulink platform encapsulated within a mimic AI-Agent shell encoded in the Matlab environment. The simple electricity market agency illustrates how agents with competing goals based on partial domain knowledge could interact to realistically simulate the processes within an actual auction.