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Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding
Benedikt Wagner · Artur Garcez

We propose neural-symbolic integration for abstract concept explanation and interactive learning. Neural-symbolic integration and explanation allow users and domain-experts to learn about the data-driven decision making process of large neural models. The models are queried using a symbolic logic language. Interaction with the user then confirms or rejects a revision of the neural model using logic-based constraints that can be distilled into the model architecture.

Author Information

Benedikt Wagner (City, University of London)
Artur Garcez (City, University of London)

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