The purpose of our research is to enhance the efficiency of AI
problem solvers by automating representation changes. We have
developed a system that improves the description of input problems
and selects an appropriate search algorithm for each given problem.
Motivation. Researchers have accumulated much evidence on the impor
tance of appropriate representations for the efficiency of AI
systems. The same problem may be easy or difficult, depending on
the way we describe it and on the search algorithm we use. Previous
work on the automatic im provement of problem descriptions has
mostly been limited to the design of individual learning
algorithms. The user has traditionally been responsible for the
choice of algorithms appropriate for a given problem. We present a
system that integrates multiple description-changing and
problem-solving algorithms. The purpose of the reported work is to
formalize the concept of representation and to confirm the
following hypothesis: An effective representation-changing system
can be built from three parts: * a library of problem-solving
algorithms; * a library of algorithms that improve problem
descriptions; * a control module that selects algorithms for each
given problem.
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