Machine Learning - DISCo

CHAPTER
ANALYTICAL
LEARNING
Inductive learning methods such as neural network and decision tree learning require
a certain number of training examples to achieve a given level of generalization accuracy,
as reflected in the theoretical bounds and experimental results discussed in
earlier chapters. Analytical learning uses prior knowledge and deductive reasoning to
augment the information provided by the training examples, so that it is not subject
to these same bounds. This chapter considers an analytical learning method called
explanation-based learning (EBL). In explanation-based learning, prior knowledge
is used to analyze, or explain, how each observed training example satisfies the
target concept. This explanation is then used to distinguish the relevant features
of the training example from the irrelevant, so that examples can be generalized
based on logical rather than statistical reasoning. Explanation-based learning has
been successfully applied to learning search control rules for a variety of planning
and scheduling tasks. This chapter considers explanation-based learning when the
learner's prior knowledge is correct and complete. The next chapter considers combining
inductive and analytical learning in situations where prior knowledge is only
approximately correct.
11.1 INTRODUCTION
Previous chapters have considered a variety of inductive learning methods: that is,
methods that generalize from observed training examples by identifying features
that empirically distinguish positive from negative training examples. Decision
tree learning, neural network learning, inductive logic programming, and genetic