The study of human cognition and the study of artificial intelligence (AI) have a symbiotic relationship, with advancements in one field often informing or creating new work in the other. Human cognition has many capabilities modern AI systems cannot compete with. One such capability is the detection, identification, and resolution of knowledge gaps (KGs). Using these capabilities as inspiration, we examine how to incorporate detection, identification, and resolution of KGs in artificial agents. We present a paradigm that enables research on the understanding of KGs for visual-linguistic communication. We leverage and enhance and existing KG taxonomy to identify possible KGs that can occur for visual question answer (VQA) tasks and use these findings to develop a classifier to identify questions that could be engineered to contain specific KG types for other VQA datasets. Additionally, we examine the performance of different VQA models through the lens of KGs.

中文翻译：

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知识差距：基于代理的自动任务完成的挑战

人类认知研究和人工智能 (AI) 研究具有共生关系，一个领域的进步通常会为另一个领域提供信息或创造新的工作。人类认知具有许多现代人工智能系统无法匹敌的能力。一种这样的能力是知识差距 (KG) 的检测、识别和解决。以这些能力为灵感，我们研究了如何结合检测、识别和解决人工代理中的 KG。我们提出了一种范式，可以研究对视觉语言交流的 KG 的理解。我们利用和增强现有的 KG 分类法来识别可能出现在视觉问答 (VQA) 任务中的 KG，并使用这些发现来开发分类器来识别可以设计为包含其他 VQA 数据集的特定 KG 类型的问题。此外，我们通过 KG 的镜头检查不同 VQA 模型的性能。