Evolving under tasks of incomplete information: streaming and self play

一般来说，只有部分可观察的任务对机器学习和遗传编程（GP）提出了独特的挑战。将从与伙伴组织相关的两个特定应用程序上下文考虑部分可观察应用程序的概念。第一个上下文是有限标签可用性下的流数据分析。目标是从表示任务当前状态的标记数据的一些小子集开始，然后能够识别生成数据流的非平稳过程的底层性质何时发生了变化。在识别流行为中的这种更改后，当前的GP解决方案应该有选择地请求与更改相对应的数据标签。然后需要做出决定，确定哪些GP个人目前被雇用来检测更改/标记流程，应该更新或替换。

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.