Probabilistic user modeling is essential for building collaborative AI
systems within probabilistic frameworks. However, modern advanced user models,
often designed as cognitive behavior simulators, are computationally
prohibitive for interactive use in cooperative AI assistants. In this extended
abstract, we address this problem by introducing widely-applicable
differentiable surrogates for bypassing this computational bottleneck; the
surrogates enable using modern behavioral models with online computational cost
which is independent of their original computational cost. We show
experimentally that modeling capabilities comparable to likelihood-free
inference methods are achievable, with over eight orders of magnitude reduction
in computational time. Finally, we demonstrate how AI-assistants can
computationally feasibly use cognitive models in a previously studied
menu-search task.

本文介绍了一种通过不同 iable surrogates 来规避计算时间瓶颈的方法，这种方法可以使现代行为模型在线使用而无需考虑其计算成本，并且在计算时间上可以实现与无法似然推断方法相当的建模能力，最后，我们展示了如何使 AI 助手在先前研究过的菜单搜索任务中计算模型的可行性。