Global Musical Tempo Transformations using Case Based ... - OFAI

Another issue is assuring the consistency of the case base. A case base
inconsistency occurs when the case base contains two cases with the same
problem description, but contradictory solutions. Obviously, different solutions
may not always be contradictory, since in many domains (e.g. expressive
music performance) there may be multiple valid solutions. But for analytical
CBR tasks such as classification, assigning contradictory labels to the same
problem is a realistic possibility. Assuming the correctness of the solution in
both cases, an inconsistency may indicate an incorrect problem description
(causing different problems to be represented in identical ways).
In a special issue of Computational Intelligence on maintaining case based
reasoning systems [28], Wilson and Leake [124] propose a general framework
for maintenance of CBR systems. To categorize maintenance policies, they
divide the maintenance process into three phases: 1) data collection, 2) triggering,
and 3) execution. During data collection, the maintainer gathers
information about the state and performance. This information is used in a
triggering phase to decide whether an actual maintenance operation should
be carried out, and selects an appropriate operation from a set of possible
operator types. In the execution phase, this operation is interpreted to the
actual situation of the CBR system, to describe when and how the operation
should be executed. The choices that are made in the general process of
maintenance depend on the goals and constraints of the CBR system that
is being maintained, such as problem solving efficiency goals, solution quality
goals, size limits of the case base, or the expected distribution of future
problems. Different combinations of goals and constraints lead to different
maintenance policies. It should be noted that the maintenance does not apply
solely to the case base itself, it may also operate on other knowledge
containers, such as retrieval and reuse components of the CBR system.
3.4 KI-CBR
As mentioned in the beginning of this chapter, the spectrum of Instance
Based Learning techniques contains rather simple techniques like Nearest-
Neighbor Learning at one end of the spectrum. At the other end, we find
the types of CBR that make thorough use of knowledge about the problem
domain, the so-called Knowledge-Intensive CBR (KI-CBR). In this section
we will focus on this kind of CBR.
In real-world problems (like medical diagnosis, investment planning, and
geological planning, but also expressive music transformation), approaches
to problem solving that select and adapt cases only using syntactical criteria
are often not adequate. Rather, a semantical approach may be needed. In
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