Autonomous decision making in scientific exploration

观察科学有一个问题。 我们知道的太多了。 电子学的最新进展，特别是在相机和光谱传感器设计方面，已经能够收集前所未有的信息量。不幸的是，收集和存储传感器信息的能力远远超过了分析信息并从中得出有意义的科学推论的能力，在大多数地球环境中，这个问题只是令人恼火。希望对世界偏远地区进行观测的科学家必须亲自前往这些偏远地区（这既昂贵又危险），或者煞费苦心地仔细研究遥感器获得的大量数据，其中大多数从科学角度来看并不有趣，因为这些数据要么不包含对感兴趣现象的观测结果，要么只是概括以前的观测结果。在行星际探索的背景下，需要额外的费用，这被称为“数据预算”。即使行星漫游者的任务变得越来越有能力，增强的动力装置和显着升级的传感器套件，低带宽的通信所施加的行星际距离意味着科学和工程团队被迫进入一种日常分类谈判，以确定“轮到谁使用天线”？在数据预算有限的情况下，减少传输数据所浪费的时间变得更加重要，因为数据不包含新信息，因此价值很小。简而言之，这是一个经典的工程问题。我们有两种宝贵而有限的资源：训练有素的科学家的认知能力和注意力，以及行星际机器人的带宽。我们提出的研究计划的长期目标是开发一套工具，利用计算机视觉和机器学习的现代技术，可以自动执行某些科学任务，以最大限度地提高调查的科学回报。这项研究并没有试图“取代”人类科学家。机器学习领域还远远没有能力制定，推理，测试和讨论假设的重要性。然而，我们认为科学界需要先进的工具，以提高研究人员搜索大型数据集的能力，特别是大型视觉和光谱数据集，以识别感兴趣的特征。我们设想这些工具充当“兴趣过滤器”，筛选TB级的数据，并在观察到某些期望的现象时通知人类。拟议的研究计划是跨学科的。通过加速信息收集和分析过程，将在应用这项工作的观测科学（行星科学、地质学、天文学等）中感受到主要影响，而且要处理的大多数具体项目都是出于与科学同事合作查明的问题。

The observational sciences have a problem.  We know too much.  Recent advances in electronics, and especially in camera and spectral sensor design have enabled the collection of unprecedented quantities of information. Unfortunately, the ability to collect and store sensor information have far outstripped the ability to analyze it and draw meaningful scientific inferences from it. In most terrestrial contexts, the problem is merely irritating. Scientists who wish to make observations of remote parts of the world must either physically travel to those remote locations (which is costly and dangerous) or painstakingly pore over large quantities of data obtained by remote sensors, most of which is not interesting from a scientific perspective, because it either contains no observations of the phenomenon of interest or merely recapitulates prior observations. In the context of interplanetary exploration, there is an additional cost imposed, which is called the "data budget". Even as planetary rover missions become more and more capable, with enhanced power plants and significantly upgraded sensor suites, the low bandwidth communications imposed by interplanetary distances mean that science and engineering teams are forced into a sort of daily triage negotiation to determine "whose turn is it to use the antenna"? It becomes even more important, in the context of a limited data budget, to reduce the wasted time spent transmitting data that, since it contains no new information, has very little value. In short, here is a classic engineering problem. We have two precious, limited resources: the cognitive capability and attention span of trained scientists, and interplanetary robot bandwidth. The long term goal of our proposed research program is is to develop a suite of tools, taking advantage of modern techniques in computer vision and machine learning, that can automate certain scientific tasks to maximize the scientific return from investigation. There is no attempt in this research to "replace" human scientists. The field of machine learning is very far from the ability to formulate, reason about, test and discuss the importance of hypotheses. However, we believe there is a need in the scientific community for advanced tools that enhance an investigator's ability to search a large dataset, especially large visual and spectral datasets, to identify features of interest. We envision these tools acting as "interest filters", sifting through terabytes of data and notifying a human when some desired phenomenon is observed. The proposed research program is interdisciplinary. The primary impacts will be felt in the observational sciences (planetary science, geology, astronomy, etc) where the work will be applied, by accelerating the process of information collection and analysis, and most of the specific projects to be addressed are motivated by problems identified in collaboration with scientific colleagues.