Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding

• URL: http://arxiv.org/abs/2112.11805v1
• Date: Wed, 22 Dec 2021 11:24:48 GMT
• Title: Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding
• Authors: Benedikt Wagner, Artur d'Avila Garcez
• Abstract summary: We propose neural-symbolic integration for abstract concept explanation and interactive learning. Interaction with the user confirms or rejects a revision of the neural model. The approach is illustrated using the Logic Network framework alongside Concept Activation Vectors and applied to a Conal Neural Network.
• Score: 1.14219428942199
• License: http://creativecommons.org/licenses/by/4.0/
• 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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