Deep analogical inference as the origin of hypotheses
Mark Blokpoel, Todd Wareham, Pim Haselager, Ivan Toni, Iris van Rooij · 2018
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 model of abduction proper that unifies the first six of these properties and lays the ground work for the seventh property computational tractability. We conjecture that abduction proper is best seen as a process of deep analogical inference.