Situation Development in a Complex Real-World Domain

Abstract

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. keywords: situation development, pre-processing 1 Introduction We are investigating the use of various Machine Learning techniques in support of the task of situation development in the domain of real-world "mock" training battles conducted by the US Army. Situation development is a process in...