• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Computational brain & behavior >Leveraging Machine Learning to Automatically Derive Robust Decision Strategies from Imperfect Knowledge of the Real World
【24h】

Leveraging Machine Learning to Automatically Derive Robust Decision Strategies from Imperfect Knowledge of the Real World

机译：利用机器学习从对现实世界的不完全了解中自动得出稳健的决策策略

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Abstract Teaching people clever heuristics is a promising approach to improve decision-making under uncertainty. The theory of resource rationality makes it possible to leverage machine learning to discover optimal heuristics automatically. One bottleneck of this approach is that the resulting decision strategies are only as good as the model of the decision problem that the machine learning methods were applied to. This is problematic because even domain experts cannot give complete and fully accurate descriptions of the decisions they face. To address this problem, we develop strategy discovery methods that are robust to potential inaccuracies in the description of the scenarios in which people will use the discovered decision strategies. The basic idea is to derive the strategy that will perform best in expectation across all possible real-world problems that could have given rise to the likely erroneous description that a domain expert provided. To achieve this, our method uses a probabilistic model of how the description of a decision problem might be corrupted by biases in human judgment and memory. Our method uses this model to perform Bayesian inference on which real-world scenarios might have given rise to the provided descriptions. We applied our Bayesian approach to robust strategy discovery in two domains: planning and risky choice. In both applications, we find that our approach is more robust to errors in the description of the decision problem and that teaching the strategies it discovers significantly improves human decision-making in scenarios where approaches ignoring the risk that the description might be incorrect are ineffective or even harmful. The methods developed in this article are an important step towards leveraging machine learning to improve human decision-making in the real world because they tackle the problem that the real world is fundamentally uncertain.
机译：抽象的人聪明的启发式教学是一个有前途的方法来改善决策下的不确定性。理性可以利用机器发现最优的启发式学习自动。只是,结果决定策略一样好模型的决策问题机器学习方法应用到。这是一个问题,因为即使是领域专家不能完整和完全准确他们面临的决策的描述。解决这个问题,我们开发的策略发现强大的潜在的方法不准确的描述场景人们将使用发现的决定策略。策略,将执行最好的期望在所有可能的现实问题可能导致错误的吗领域专家提供的描述。实现这一目标,我们的方法使用一个概率模型描述的一个决定问题可能会破坏人类的偏见判断和记忆。执行实际的贝叶斯推理场景可能导致提供的描述。健壮的策略发现在两个领域:规划和冒险的选择。我们发现我们的方法更健壮错误的决策问题的描述它发现,教学策略极大地提高了人类的决策场景方法忽略的风险描述可能是不正确的无效的甚至是有害的。本文开发的一个重要的步骤对利用机器学习来提高人类的决策在现实世界中,因为他们解决现实世界的问题从根本上不确定。

著录项

相似文献

  • 外文文献
  • 中文文献
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号