Machine Learning - DISCo

explaining to itself the reason for this conflict and creating a rule such as the one
above. The net effect is that PRODIGY uses domain-independent knowledge about
possible subgoal conflicts, together with domain-specific knowledge of specific
operators (e.g., the fact that the robot can pick up only one block at a time), to
learn useful domain-specific planning rules such as the one illustrated above.
The use of explanation-based learning to acquire control knowledge for
PRODIGY has been demonstrated in a variety of problem domains including the
simple block-stacking problem above, as well as more complex scheduling and
planning problems. Minton (1988) reports experiments in three problem domains,
in which the learned control rules improve problem-solving efficiency by a factor
of two to four. Furthermore, the performance of these learned rules is comparable
to that of handwritten rules across these three problem domains. Minton also describes
a number of extensions to the basic explanation-based learning procedure
that improve its effectiveness for learning control knowledge. These include methods
for simplifying learned rules and for removing learned rules whose benefits
are smaller than their cost.
A second example of a general problem-solving architecture that incorporates
a form of explanation-based learning is the SOAR system (Laird et al. 1986;
Newel1 1990). SOAR supports a broad variety of problem-solving strategies that
subsumes PRODIGY'S means-ends planning strategy. Like PRODIGY, however, SOAR
learns by explaining situations in which its current search strategy leads to inefficiencies.
When it encounters a search choice for which it does not have a definite
answer (e.g., which operator to apply next) SOAR reflects on this search impasse,
using weak methods such as generate-and-test to determine the correct course of
action. The reasoning used to resolve this impasse can be interpreted as an explanation
for how to resolve similar impasses in the future. SOAR uses a variant of
explanation-based learning called chunking to extract the general conditions under
which the same explanation applies. SOAR has been applied in a great number
of problem domains and has also been proposed as a psychologically plausible
model of human learning processes (see Newel1 1990).
PRODIGY and SOAR demonstrate that explanation-based learning methods can
be successfully applied to acquire search control knowledge in a variety of problem
domains. Nevertheless, many or most heuristic search programs still use numerical
evaluation functions similar to the one described in Chapter 1, rather than rules
acquired by explanation-based learning. What is the reason for this? In fact, there
are significant practical problems with applying EBL to learning search control.
First, in many cases the number of control rules that must be learned is very large
(e.g., many thousands of rules). As the system learns more and more control rules
to improve its search, it must pay a larger and larger cost at each step to match this
set of rules against the current search state. Note this problem is not specific to
explanation-based learning; it will occur for any system that represents its learned
knowledge by a growing set of rules. Efficient algorithms for matching rules can
alleviate this problem, but not eliminate it completely. Minton (1988) discusses
strategies for empirically estimating the computational cost and benefit of each
rule, learning rules only when the estimated benefits outweigh the estimated costs