Machine Learning - Special issue on learning with probabilistic representations
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Categorizing Evolved CoreWar Warriors Using EM and Attribute Evaluation
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
| Hi-index | 0.00 |
CoreWar is a computer simulation devised in the 1980s where programs loaded into a virtual memory array compete for control over the virtual machine. These programs are written in a special-purpose assembly language called Redcodeand referred to as warriors. A great variety of environments and battle strategies have emerged over the years, leading to formation of different warrior types. This paper deals with the problem of automatic warrior categorization, presenting results of classification based on several approaches to warrior representation, and offering insight into ambiguities concerning the identification of strategic classes. Over 600 human-coded warriors were annotated, forming a training set for classification. Several major classifiers were used, SVMs proving to be the most reliable, reaching accuracy of 84%. Classification of an evolved warrior set using the trained classifiers was also conducted. The obtained results proved helpful in outlining the issues with both automatic and manual Redcode program categorization.