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arXiv · 2112.11805

Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding

Benedikt Wagner·Artur d'Avila Garcez
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Abstract

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. The approach is illustrated using the Logic Tensor Network framework alongside Concept Activation Vectors and applied to a Convolutional Neural Network.

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Benedikt Wagner, Artur d'Avila Garcez. 2021-12-22. Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding. https://arxiv.org/abs/2112.11805

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