Autonomous decision making in scientific exploration

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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.