  An autonomous experimentation platform in manufacturing is supposedly capable
of conducting a sequential search for finding suitable manufacturing conditions
for advanced materials by itself or even for discovering new materials with
minimal human intervention. The core of the intelligent control of such
platforms is the policy directing sequential experiments, namely, to decide
where to conduct the next experiment based on what has been done thus far. Such
policy inevitably trades off exploitation versus exploration and the current
practice is under the Bayesian optimization framework using the expected
improvement criterion or its variants. 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 or a design place under resource
constraints. We argue that such capability is much needed for futuristic
autonomous experimentation platforms. We do not claim that we have a fully
autonomous experimentation platform, but believe that our current effort sheds
new lights or provides a different view angle as researchers are racing to
elevate the autonomy of various primitive autonomous experimentation systems.
