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Application of Analogical Reasoning for use in Visual Knowledge Extraction

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[Technical Report, Master's Thesis]

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There is a continual push to make Artificial Intelligence AI as human-like as possible however, this is a difficult task because of its inability to learn beyond its current comprehension. Analogical reasoning AR has been proposed as one method to achieve this goal. Current literature lacks a technical comparison on psychologically-inspired and natural-language-processing-produced AR algorithms with consistent metrics on multiple-choice word-based analogy problems. Assessment is based on correctness and goodness metrics. There is not a one-size-fits-all algorithm for all textual problems. As contribution in visual AR, a convolutional neural network CNN is integrated with the AR vector space model, Global Vectors GloVe, in the proposed, Image Recognition Through Analogical Reasoning Algorithm IRTARA. Given images outside of the CNNs training data, IRTARA produces contextual information by leveraging semantic information from GloVe. IRTARAs quality of results is measured by definition, AR, and human factors evaluation methods, which saw consistency at the extreme ends. There search shows the potential for AR to facilitate more a human-like AI through its ability to understand concepts beyond its foundational knowledge in both a textual and visual problem space.

Subject Categories:

  • Cybernetics

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[A, Approved For Public Release]