Situation Development in a Complex Real-World Domain

Applying techniques from Machine Learning to real-world domains and problems often requires considerable processing of the input data, to both remove noise and to augment the amount and type of information present. We describe our work in the task of situation assessment in the domain of US Army training exercises involving hundreds of agents interacting in real-time over the course of several days. In particular, we describe techniques we have developed to process this data and draw general conclusions on the types of information required in order to apply various Machine Learning algorithms and how this information may be extracted in real-world situations where it is not directly represented.

Read the paper:

Situation Development in a Complex Real-World Domain

by Mark Devaney and Ashwin Ram

International Conference on Machine Learning (ICML-97) Workshop on Machine Learning Applications in the Real World, Nashville, TN, July 1997
www.cc.gatech.edu/faculty/ashwin/papers/er-97-05.pdf
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