MMO Smart Servers Using Neural Networks for Intelligent, Client-handling Decisions and Interactions

This paper proposes a complex, adaptive System of Systems architecture whose goal is to intelligently and dynamically host Massive Multi-Player Online (MMO) games. Furthermore, the use of a Perceptron employed with a Sigmoidal Membership Function trained by the Least Mean Squares learning algorithm is proposed. The network intelligently manages communications and interactions between equal, subordinate, and client level servers based on a reward returned by each respective neural network. The success of these techniques allows for a fully open and interactive world with minimal server/client maximums, minimal load times, and minimal network down-time. This freedom is due to the dynamic trading of resources and/or hosted clients during execution. This paper also outlines a proof of concept application designed to demonstrate the viability of this concept. Experimental results show that the complex system of systems is able to quickly adapt to the quickly changing environment thereby proving its plausibility as an adaptive and dynamic server management system.