Categorizing Evolved CoreWar Warriors Using EM and Attribute Evaluation
CoreWar is a computer simulation where two programs written in an assembly language called redcode compete in a virtual memory array. These programs are referred to as warriors. Over more than twenty years of development a number of different battle strategies have emerged, making it possible to identify different warrior types. Systems for automatic warrior creation appeared more recently, evolvers being the dominant kind. This paper describes an attempt to analyze the output of the CCAI evolver, and explores the possibilities for performing automatic categorization by warrior type using representations based on redcode source, as opposed to instruction execution frequency. Analysis was performed using EM clustering, as well as information gain and gain ratio attribute evaluators, and revealed which mainly brute-force types of warriors were being generated. This, along with the observed correlation between clustering and the workings of the evolutionary algorithm justifies our approach and calls for more extensive experiments based on annotated warrior benchmark collections.
KeywordsExpectation Maximization Information Gain Expectation Maximization Algorithm Complete Dataset Attribute Evaluation
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