Integrating Multiple Learning Components Through Markov Logic
Thomas G. Dietterich
and
Xinlong Bao
Abstract:
This paper addresses the question of how statistical
learning algorithms can be integrated into a larger
AI system both from a practical engineering perspective
and from the perspective of correct representation,
learning, and reasoning. Our goal is to create an integrated
intelligent system that can combine observed
facts, hand-written rules, learned rules, and learned
classifiers to perform joint learning and reasoning. Our
solution, which has been implemented in the CALO
system, integrates multiple learning components with
a Markov Logic inference engine, so that the components
can benefit from each other’s predictions. We
introduce two designs of the learning and reasoning
layer in CALO: the MPE Architecture and the Marginal
Probability Architecture. The architectures, interfaces,
and algorithms employed in our two designs are described,
followed by experimental evaluations of the
performance of the two designs. We show that by integrating
multiple learning components through Markov
Logic, the performance of the system can be improved
and that theMarginal Probability Architecture performs
better than the MPE Architecture.
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