Experimental design in field robotics is an adaptive human-in-the-loop
decision-making process in which an experimenter learns about system
performance and limitations through interactions with a robot in the form of
constructed experiments. This can be challenging because of system complexity,
the need to operate in unstructured environments, and the competing objectives
of maximizing information gain while simultaneously minimizing experimental
costs. Based on the successes in other domains, we propose the use of a
Decision Support System (DSS) to amplify the human's decision-making abilities,
overcome their inherent shortcomings, and enable principled decision-making in
field experiments. In this work, we propose common terminology and a six-stage
taxonomy of DSSs specifically for adaptive experimental design of more
informative tests and reduced experimental costs. We construct and present our
taxonomy using examples and trends from DSS literature, including works
involving artificial intelligence and Intelligent DSSs. Finally, we identify
critical technical gaps and opportunities for future research to direct the
scientific community in the pursuit of next-generation DSSs for experimental
design.

基于决策支持系统（DSS）在其他领域中的成功案例，本文为自适应实验设计提出了通用术语和一个六阶段的 DSS 分类标准。通过人 - 机交互，旨在最大化信息获取，同时尽量减少实验成本，我们提出了使用 DSS 增强人类决策能力的方法，以协助决策制定者作出更坚实的决策。