Zusammenfassung
As the potential of applying machine learning
techniques to perplexing problems is realised,
increasingly complex problems are being tackled,
requiring intricate explanations to be induced. Escher
is a functional logic language whose higher-order
constructs allow arbitrarily complex observations to be
captured and highly expressive generalisations to be
conveyed.
The work presented in this thesis alleviates the
challenging problem of identifying an underlying
structure normally required to search the resulting
hypothesis space efficiently. This is achieved through
STEPS, an evolutionary based system that allows the
vast space of highly expressive Escher programs to be
explored. STEPS provides a natural upgrade of the
evolution of concept descriptions to the higher-order
level.
In particular STEPS uses the individual-as-terms
approach to knowledge representation where all the
information provided by an example is localised as a
single closed term so that examples of arbitrary
complexity can be treated in a uniform manner. STEPS
also supports Lambda abstractions as arguments to
higher-order functions thus enabling the invention of
new functions not contained in the original alphabet.
Finally, STEPS provides a number of specialised genetic
operators for the design of specific concept learning
strategies.
STEPS has been successfully applied to a number of
complex real world problems, including the
international PTE2 challenge. This problem involves the
prediction of the Carcinogenic activity of a test set
of 30 chemical compounds. The results produced by STEPS
rank joint second if the hypothesis must be
interpretable and joint first if interpretability is
sacrificed for increased accuracy.
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