Evolving under tasks of incomplete information: streaming and self play

只有部分可观察到的任务对机器学习，特别是遗传编程(GP)提出了独特的挑战。部分可观察到的应用的概念将从与伙伴组织相关的两个具体应用背景来考虑。第一种情况是在有限的标签可用性下的流数据分析。目标是从代表任务当前状态的标记数据的某个小子集开始，然后能够识别生成数据流的非静态过程的基本性质何时发生了变化。在识别到流的行为中的这种变化时，当前的GP解决方案应该选择性地请求对应于该变化的数据的标签。然后需要做出决定，以确定应更新或替换哪些当前受雇于检测流变化/标签的全科医生。

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.