User models play an important role in interaction design, supporting automation of interaction design choices. In order to do so, model parameters must be estimated from user data. While very large amounts of user data are sometimes required, recent research has shown how experiments can be designed so as to gather data and infer parameters as efficiently as possible, thereby minimising the data requirement. In the current article, we investigate a variant of these methods that amortises the computational cost of designing experiments by training a policy for choosing experimental designs with simulated participants. Our solution learns which experiments provide the most useful data for parameter estimation by interacting with in-silico agents sampled from the model space thereby using synthetic data rather than vast amounts of human data. The approach is demonstrated for three progressively complex models of pointing.

翻译：用户模型在人机交互设计中扮演着重要角色，支持交互设计决策的自动化。为实现这一目标，模型参数必须从用户数据中估计得出。尽管有时需要大量用户数据，但近期研究已展示如何通过优化实验设计来高效收集数据并推断参数，从而最大限度地降低数据需求。本文研究了一种改进方法，该方法通过训练一个策略来与模拟参与者交互以选择实验设计，从而摊销实验设计的计算成本。我们的方案通过与从模型空间中采样的虚拟智能体交互，利用合成数据而非海量人类数据，学习哪些实验能为参数估计提供最有价值的数据。该方法的有效性通过三个复杂度递增的指向性模型得到了验证。