Using cortically-inspired algorithms for analogical learning and reasoning
We consider the neurologically-inspired hypothesis that higher level cognition is built on the same fundamental building blocks as low-level perception. That is, the same basic algorithm that is able to represent and perform inference on low-level sensor data can also be used to process relational structures. We present a system that represents relational structures as feature bags. Using this representation, our system leverages algorithms inspired by the sensory cortex to automatically create an ontology of relational structures and to efficiently retrieve analogs for new relational structures from long-term memory. We provide a demonstration of our approach that takes as input a set of unsegmented stories, constructs an ontology of analogical schemas (corresponding to plot devices), and uses this ontology to find analogs within new stories in time logarithmic in the total number of stories, yielding significant time-savings over linear analog retrieval with only a small sacrifice in accuracy. We also provide a proof of concept for how our framework allows for cortically-inspired algorithms to perform analogical inference. Finally, we discuss how insights from our system can be used so that a cortically-inspired model can serve as the core mechanism for a full cognitive architecture.
analogy cortical models ontology learning binding problem