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首页> 外文期刊>The Journal of Problem Solving >Deep Analogical Inference as the Origin of Hypotheses
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Deep Analogical Inference as the Origin of Hypotheses

机译：深度类推推理作为假设的起源

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The ability to generate novel hypotheses is an important problem-solving capacity of humans. This ability is vital for making sense of the complex and unfamiliar world we live in. Often, this capacity is characterized as an inference to the best explanation—selecting the “best” explanation from a given set of candidate hypotheses. However, it remains unclear where these candidate hypotheses originate from. In this paper we contribute to computationally explaining these origins by providing the contours of the computational problem solved when humans generate hypotheses. The origin of hypotheses, otherwise known as abduction proper, is hallmarked by seven properties: (1) isotropy, (2) open-endedness, (3) novelty, (4) groundedness, (5) sensibility, (6) psychological realism, and (7) computational tractability. In this paper we provide a computational-level theory of abduction proper that unifies the first six of these properties and lays the groundwork for the seventh property of computational tractability. We conjecture that abduction proper is best seen as a process of deep analogical inference.
机译：产生新假设的能力是人类重要的解决问题的能力。这种能力对于弄清我们所生活的复杂而陌生的世界至关重要。通常，这种能力的特征是对最佳解释的推论-从给定的候选假设集中选择“最佳”解释。但是，尚不清楚这些候选假设来自何处。在本文中，我们通过提供人类生成假设时可以解决的计算问题的轮廓，为在计算上解释这些起源做出贡献。假设的起源（也称为绑架固有）具有以下七个特征：（1）各向同性，（2）开放性，（3）新奇，（4）扎根，（5）敏感性，（6）心理现实主义， （7）计算可处理性。在本文中，我们提供了适当的计算级别绑架理论，该理论统一了这些属性的前六个属性，并为计算可处理性的第七个属性奠定了基础。我们推测，最好将绑架视为深度类推推理的过程。

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