Machine Learning - DISCo

that fits the learner's prior knowledge and covers the training examples.
Humans often make use of prior knowledge to guide the formation of new
hypotheses. This chapter examines purely analytical learning methods. The
next chapter examines combined inductive-analytical learning.
a Explanation-based learning is a form of analytical learning in which the
learner processes each novel training example by (1) explaining the observed
target value for this example in terms of the domain theory, (2) analyzing this
explanation to determine the general conditions under which the explanation
holds, and (3) refining its hypothesis to incorporate these general conditions.
a PROLOG-EBG is an explanation-based learning algorithm that uses first-order
Horn clauses to represent both its domain theory and its learned hypotheses.
In PROLOG-EBG an explanation is a PROLOG proof, and the hypothesis
extracted from the explanation is the weakest preimage of this proof. As a
result, the hypotheses output by PROLOG-EBG follow deductively from its
domain theory.
a Analytical learning methods such as PROLOG-EBG construct useful intermediate
features as a side effect of analyzing individual training examples. This
analytical approach to feature generation complements the statistically based
generation of intermediate features (eg., hidden unit features) in inductive
methods such as BACKPROPAGATION.
a Although PROLOG-EBG does not produce hypotheses that extend the deductive
closure of its domain theory, other deductive learning procedures can.
For example, a domain theory containing determination assertions (e.g., "nationality
determines language") can be used together with observed data to
deductively infer hypotheses that go beyond the deductive closure of the
domain theory.
a One important class of problems for which a correct and complete domain
theory can be found is the class of large state-space search problems. Systems
such as PRODIGY and SOAR have demonstrated the utility of explanationbased
learning methods for automatically acquiring effective search control
knowledge that speeds up problem solving in subsequent cases.
a Despite the apparent usefulness of explanation-based learning methods in
humans, purely deductive implementations such as PROLOG-EBG suffer the
disadvantage that the output hypothesis is only as correct as the domain
theory. In the next chapter we examine approaches that combine inductive
and analytical learning methods in order to learn effectively from imperfect
domain theories and limited training data.
The roots of analytical learning methods can be traced to early work by
Fikes et al. (1972) on learning macro-operators through analysis of operators
in ABSTRIPS and to somewhat later work by Soloway (1977) on the use of
explicit prior knowledge in learning. Explanation-based learning methods similar
to those discussed in this chapter first appeared in a number of systems developed
during the early 1980s, including DeJong (1981); Mitchell (1981); Winston et al.