Toward Futuristic Autonomous Experimentation—A Surprise-Reacting Sequential Experiment Policy

Abstract

An autonomous experimentation platform in manufacturing is supposedly capable of conducting a sequential search for finding suitable manufacturing conditions by itself or even for discovering new materials with minimal human intervention. The core of the intelligent control of such platforms is a policy to decide where to conduct the next experiment based on what has been done thus far. Such policy inevitably trades off between exploitation and exploration. Currently, the prevailing approach is to use various acquisition functions in the Bayesian optimization framework. We discuss whether it is beneficial to trade off exploitation versus exploration by measuring the element and degree of surprise associated with the immediate past observation. We devise a surprise-reacting policy using two existing surprise metrics, known as the Shannon surprise and Bayesian surprise. Our analysis shows that the surprise-reacting policy appears to be better suited for quickly characterizing the overall landscape of a response surface under resource constraints. We do not claim that we have a fully autonomous experimentation system but believe that the surprise-reacting capability benefits the automation of sequential decisions in autonomous experimentation.Note to Practitioners Autonomous systems should be able to go beyond repetitive automatic actions that are generally pre-programmed through a recipe. To decide what to do next on the fly differentiates autonomy from automation. Arguably, autonomy is the highest form of automation. To endow a manufacturing with autonomy, one necessary capability is for it to react properly to the “unexpected,” which are those observations disagreeing with its model’s anticipation. Are these bad measurements, an anomaly, or an indicator of model inadequacy? Should the observations be discarded or should the model be updated using the new observation? If latter, should model be updated gradually overtime or radically altered? Figuratively, upon observing the unexpected, we say that a manufacturing control system is “surprised” and ask the question of how it should react. Our investigation shares our current insights on this question.