Integrating Multiple Learning Components Through Markov Logic

Thomas G. Dietterich and Xinlong Bao


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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