Mining Closed and Maximal Frequent Subtrees from Databases of Labeled Rooted Trees
IEEE Transactions on Knowledge and Data Engineering
On Intelligence
Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference
Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Deep Spatiotemporal Feature Learning with Application to Image Classification
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Perception processing for general intelligence: bridging the symbolic/subsymbolic gap
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
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The bridging of the gap between 1) subsymbolic pattern recognition and learning algorithms and 2) symbolic reasoning algorithms, has been a major issue for AI since the early days of the field. One class of approaches involves integrating subsymbolic and symbolic systems, but this raises the question of how to effectively translate between the very different languages involved. In the approach described here, a frequent subtree mining algorithm is used to identify recurrent patterns in the state of a hierarchical deep learning system (DeSTIN) that is exposed to visual stimuli. The relationships between state-subtrees and percepts are then input to a probabilistic logic system (OpenCog's Probabilistic Logic Networks), which conducts uncertain inferences using them as axioms. The core conceptual idea is to use patterns in the states inferred by a perceptual hierarchy, as inputs to an uncertain logic system. Simple illustrative examples are presented based on the presentation of images of typed letters to DeSTIN. This work forms a component of a larger project to integrate perceptual, motoric and cognitive processing within the integrative OpenCog cognitive architecture.