Machine Learning - DISCo

(1983); and Silver (1983). DeJong and Mooney (1986) and Mitchell et al. (1986)
provided general descriptions of the explanation-based learning paradigm, which
helped spur a burst of research on this topic during the late 1980s. A collection of
research on explanation-based learning performed at the University of Illinois is
described by DeJong (1993), including algorithms that modify the structure of the
explanation in order to correctly generalize iterative and temporal explanations.
More recent research has focused on extending explanation-based methods to
accommodate imperfect domain theories and to incorporate inductive together
with analytical learning (see Chapter 12). An edited collection exploring the role
of goals and prior knowledge in human and machine learning is provided by Ram
and Leake (1995), and a recent overview of explanation-based learning is given
by DeJong (1997).
The most serious attempts to employ explanation-based learning with perfect
domain theories have been in the area of learning search control, or "speedup"
learning. The SOAR system described by Laird et al. (1986) and the PRODIGY
system described by Carbonell et al. (1990) are among the most developed systems
that use explanation-based learning methods for learning in problem solving.
Rosenbloom and Laird (1986) discuss the close relationship between SOAR'S
learning method (called "chunking") and other explanation-based learning methods.
More recently, Dietterich and Flann (1995) have explored the combination
of explanation-based learning with reinforcement learning methods for learning
search control.
While our primary purpose here is to study machine learning algorithms, it
is interesting to note that experimental studies of human learning provide support
for the conjecture that human learning is based on explanations. For example,
Ahn et al. (1987) and Qin et al. (1992) summarize evidence supporting the conjecture
that humans employ explanation-based learning processes. Wisniewski and
Medin (1995) describe experimental studies of human learning that suggest a rich
interplay between prior knowledge and observed data to influence the learning
process. Kotovsky and Baillargeon (1994) describe experiments that suggest even
11-month old infants build on prior knowledge as they learn.
The analysis performed in explanation-based learning is similar to certain
kinds of program optimization methods used for PROLOG programs, such as partial
evaluation; van Harmelen and Bundy (1988) provide one discussion of the
relationship.
EXERCISES
11.1. Consider the problem of learning the target concept "pairs of people who live in
the same house," denoted by the predicate HouseMates(x, y). Below is a positive
example of the concept.
HouseMates(Joe, Sue)
Person( Joe)
Sex(Joe, Male)
Hair Color (Joe, Black)
Person(Sue)
Sex(Sue, Female)
Haircolor (Sue, Brown)