This book describes a method for building real-world problem
solving systems such as medical diagnostic procedures and
intelligent controllers for autonomous underwater vehicles (AUVs)
and other robots. The approach taken is different from other work
reported in the artificial intelligence literature in several
respects: * It defines schema-based reasoning, in which schemas --
explicitly declared packets of related knowledge -- are used to
control not only the reasoner's planning, but also all other facets
of its behavior. * It is a kind of reactive reasoning that the
author calls adaptive problem solving -- the reasoner maintains
commitments to future goals but is able to change its focus of
attention as the problem-solving situation requires. * It is a
context-sensitive reasoning method. Every decision it makes relies
on the use of contextual knowledge to be appropriate for the
current problem-solving situation. Furthermore, context is
represented explicitly; by always keeping a current representation
of the context in mind, the reasoner's behavior is automatically
sensitive to the context with very little work needed per decision.
* Schema-based reasoning -- a generalization of case-based
reasoning -- extends the usual idea of case-based reasoning to
encompass all aspects of the reasoner's behavior, and it extends it
to make use of generalized "cases" (i.e., schemas) rather than
particular cases, thus saving effort needed to transfer knowledge
from an old case to a new situation. Though the work originated in
the domain of medical diagnostic problem solving, treating
diagnosis as a planning task, it is even more appropriate for
controlling autonomous systems. The author is currently extending
the approach by creating a robust controller for long-range
autonomous underwater vehicles that will be used to carry out ocean
science missions.
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