In 250A we studied the use of logic to state knowledge, and algorithms for using knowledge, i.e. reasoning. One definition of AI is that it is the part of computer science that investigates algorithms for processing knowledge, as opposed to lower-level data and information. In supervised learning, the information available to the learner consists of labeled training examples. The knowledge to be acquired is a classifier capable of predicting the labels of examples with unavailable labels. In unsupervised learning, the information available consists of unlabeled training examples.
Scientific discovery falls under unsupervised learning.
The task here is to accelerate a problem-solving procedure. Generally the idea is to specialize the algorithm to be faster for a particular application domain. Direct speedup learning has not been very successful. A better approach may be to do supervised or unsupervised learning to acquire declarative control knowledge. The classification above is only indicative.