原始人身体结构与装置的认知归纳偏见

Chandra Bhim Bhan Singh
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引用次数: 0

摘要

对科学知识和实践的有力和深刻的解释必须考虑到人类的认知技能和约束如何使科学企业的活动和产品成为可能,也如何限制它们。虽然现有的深度学习系统在对象分类、语言处理和游戏玩法等功能方面表现出色,但很少有系统能够创建或转换像框架金字塔这样的复杂系统。假设这些系统缺乏的是“认知归纳偏见”:一种证明对象间关系的能力,以及对事件进行有组织描述的能力。为了评估这一前提,本文将重点放在一项工作上,该工作涉及将一堆框架钉在一起以平衡城堡,并量化原始人的表现。然后,为了分析装置能力,我们的工作引入了显著刺激学习工具,该工具利用了以对象和交互为中心的场景和策略表示,这些表示适用于任务。我们的研究结果表明,这些结构描述使工具能够在更朴素的方法中执行原始人和精巧装置,这表明认知归纳效应是解决结构化推理问题和为机器构建更智能更灵活的重要因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cognitive Inductive Prejudice For Corporal Edifice In Hominids And Contraption
A strong and insightful interpretation of scientific knowledge and practice must take into consideration how human cognitive skills and constraints enable as well restrict the scientific enterprise's activities and products. While existing deep learning systems are outstanding in functions such as object classification, language processing, and gameplay but few can create or transform a complex system like a Frame Pyramid. Assume that what these systems lack is a "Cognitive Inductive Prejudice": an ability to justify inter-object relationships and make decisions about an organized description of the incident. In order to assess this premise, this paper concentrated on a work involving stapling together stacks of frames to balance a castle and quantify how well hominids are doing. Then for analyzing contraption capability, our work introduce the Significant Stimulus Learning Tool that utilizes object-and interactioncentered scene and policy representations, these apply to the task. Our results shows that these structural portrayals enable the tool to perform both hominids and contraption for more naive methods, indicating that cognitive inductive effect is a significant element in solving structured reasoning issues and building more intelligent also flexible for machines.
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