论文标题
建模人类主动搜索以优化黑框功能
Modelling Human Active Search in Optimizing Black-box Functions
论文作者
论文摘要
对人类功能学习进行建模一直是认知科学领域内部研究的主题。该主题与黑框优化相关，在黑框优化中，有关目标和/或约束的信息不可用，必须通过功能评估来学习。在本文中，我们着重于贝叶斯优化中使用的最大值和概率模型的人类行为之间的关系。由于已经考虑了未知功能的替代模型，因此已经考虑了高斯过程和随机森林：贝叶斯学习范式在开发主动学习方法的开发中至关重要，在不确定的条件下平衡探索/剥削方面，在大型决策空间中有效概括。在本文中，我们通过实验分析贝叶斯优化与搜索未知2D函数的最大值的人类相比。使用两个替代模型的一组受试者进行的一组受控实验，确认贝叶斯优化提供了一种通用模型，以表示人类中主动学习的单个模式
Modelling human function learning has been the subject of in-tense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. In this paper we focus on the relation between the behaviour of humans searching for the maximum and the probabilistic model used in Bayesian Optimization. As surrogate models of the unknown function both Gaussian Processes and Random Forest have been considered: the Bayesian learning paradigm is central in the development of active learning approaches balancing exploration/exploitation in uncertain conditions towards effective generalization in large decision spaces. In this paper we analyse experimentally how Bayesian Optimization compares to humans searching for the maximum of an unknown 2D function. A set of controlled experiments with 60 subjects, using both surrogate models, confirm that Bayesian Optimization provides a general model to represent individual patterns of active learning in humans
