Probabilistic user modeling is essential for building machine learning systems in the ubiquitous cases with humans in the loop. However, modern advanced user models, often designed as cognitive behavior simulators, are incompatible with modern machine learning pipelines and computationally prohibitive for most practical applications. We address this problem by introducing widely-applicable differentiable surrogates for bypassing this computational bottleneck; the surrogates enable computationally efficient inference with modern cognitive models. We show experimentally that modeling capabilities comparable to the only available solution, existing likelihood-free inference methods, are achievable with a computational cost suitable for online applications. Finally, we demonstrate how AI-assistants can now use cognitive models for online interaction in a menu-search task, which has so far required hours of computation during interaction.

翻译：概率性用户建模对于构建包含人机交互环节的通用机器学习系统至关重要。然而，现代先进用户模型通常设计为认知行为模拟器，这类模型与现代机器学习流程不兼容，且计算开销过大，难以应用于实际场景。针对这一问题，我们通过引入广泛适用的可微分替代模型来绕过这一计算瓶颈：该替代模型能够基于现代认知模型实现高计算效率的推理。实验表明，与目前唯一的解决方案——现有无似然推断方法——相比，这种建模能力在计算成本上更适合在线应用场景。最后，我们展示了AI助手如何在菜单搜索任务中利用认知模型进行实时交互——此前的类似任务需要耗费数小时的计算时间。