Abstraction and Analogy-Making in Artificial Intelligence

Abstract

Conceptual abstraction and analogy-making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite of a long history of research on constructing AI systems with these abilities, no current AI system is anywhere close to a capability of forming humanlike abstractions or analogies. This paper reviews the advantages and limitations of several approaches toward this goal, including symbolic methods, deep learning, and probabilistic program induction. The paper concludes with several proposals for designing challenge tasks and evaluation measures in order to make quantifiable and generalizable progress in this area.

Revised version. 30 pages, 9 figures. To appear in Annals of the New York Academy of Sciences

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Cited by in corpus (6)

• A Review of Emerging Research Directions in Abstract Visual Reasoning
• Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices
• The Duality of Data and Knowledge Across the Three Waves of AI
• A Neural Approach for Detecting Morphological Analogies
• Abstraction, Reasoning and Deep Learning: A Study of the "Look and Say" Sequence
• Toward Building Science Discovery Machines