Evolving under tasks of incomplete information: streaming and self play

点击翻译按钮获取中文摘要

Tasks that only are only partially observable present unique challenges for machine learning in general and genetic programming (GP) in particular. The notion of a partially observable application will be considered from two specific application contexts of relevance to the partner organization. The first context is that of streaming data analysis under limited label availability. The goal is to begin with some small subset of labelled data representing the current status of a task, and then be able to recognize when the underlying nature of the non-stationary process generating the data stream has changed. On recognizing such a change in the stream's behaviour, the current GP solutions should selectively request labels for the data corresponding to the change. Decisions then need to be made to determine which GP individuals currently employed for detecting change / labelling the stream, should be updated or replaced.