Designing Intelligent Instruments
Kevin H. Knuth, Philip M. Erner, Scott Frasso

TL;DR
This paper presents a framework for an intelligent instrument that automates data collection and analysis using Bayesian methods and nested sampling to iteratively refine hypotheses, aiming to emulate the scientific method.
Contribution
It introduces an integrated system combining experimental design, Bayesian exploration, and inference to create an autonomous scientific instrument prototype.
Findings
Implemented a prototype using LEGO MINDSTORMS NXT
Demonstrated iterative hypothesis refinement
Showed potential for autonomous scientific data collection
Abstract
Remote science operations require automated systems that can both act and react with minimal human intervention. One such vision is that of an intelligent instrument that collects data in an automated fashion, and based on what it learns, decides which new measurements to take. This innovation implements experimental design and unites it with data analysis in such a way that it completes the cycle of learning. This cycle is the basis of the Scientific Method. The three basic steps of this cycle are hypothesis generation, inquiry, and inference. Hypothesis generation is implemented by artificially supplying the instrument with a parameterized set of possible hypotheses that might be used to describe the physical system. The act of inquiry is handled by an inquiry engine that relies on Bayesian adaptive exploration where the optimal experiment is chosen as the one which maximizes the…
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