Abstraction and analogy‐making in artificial intelligence
Abstraction and analogy‐making in artificial intelligence
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DOI：
10.1111/nyas.14619
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发表时间：
2021-02
影响因子：
5.2
通讯作者：
M. Mitchell
中科院分区：
文献类型：
--
作者：
M. Mitchell
概念抽象和类比是人类学习、推理和将知识适应新领域的关键能力。尽管在构建具有这些能力的人工智能（AI）系统方面有很长的研究历史，但目前的人工智能系统还没有接近形成类似人类的抽象或类比的能力。本文回顾了实现这一目标的几种方法的优点和局限性，包括符号方法、深度学习和概率程序归纳。文章最后提出了挑战任务设计和评价措施的若干建议，以期在该领域取得可量化和可推广的进展。
Conceptual abstraction and analogy‐making are key abilities underlying humans' abilities to learn, reason, and robustly adapt their knowledge to new domains. Despite a long history of research on constructing artificial intelligence (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.