象征性元程序搜索提高了学习效率,并解释了人类的规则学习
Joshua S Rule1, Steven T Piantadosi2, Andrew Cropper3
1Psychology, University of California, Berkeley, Berkeley, CA, 94704, USA. rule@berkeley.edu.
Nature communications
|August 10, 2024
概括
人类高效地学习复杂的规则使用metaprograms,这是修改其他程序的程序. 这种方法显著增强了象征性规则学习,与最小的计算成本密切匹配人类学习效率.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人类获得复杂的规则,以最小的数据来完成各种任务.
- 符号程序学习模型解释规则获取,但与复杂性作斗争.
- 将象征性规则学习扩展到人类水平的表现仍然是一个挑战.
研究的目的:
- 调查元编程是否可以提高符号规则学习效率.
- 在复杂的领域中模拟人类统治的获取.
- 将元编程与现有的符号学习方法进行比较.
主要方法:
- 在metaprograms (修改程序的程序) 上进行符号搜索.
- 在100个算法丰富的规则的行为基准上进行评估.
- 与其他符号规则学习模型进行比较.
主要成果:
- 与传统方法相比,元编程显著提高了学习效率.
- 这种方法在适应人类学习模式方面表现出更高的准确性.
- 所需的计算与对人类思维时间的保守估计保持一致.
结论:
- 类似于元程序的表示是有效地获得人类规则的有希望的机制.
- 这种方法为象征性规则学习提供了一个可扩展的解决方案.
- 未来的研究可以在不同的学习领域探索元编程.
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