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.

翻译：概率性用户建模对于在概率框架内建立合作性AI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,在计算上对合作性AI助理的互动使用来说是令人望而却步的。在这个扩展的抽象中,我们通过采用广泛应用的不同替代器绕过这一计算瓶颈来解决这一问题;代孕使使用现代行为模型及其在线计算成本成为可能。我们实验性地表明,可以实现与概率自由推论方法相类似的模型能力,计算时间将减少8个以上的数量级。最后,我们展示了AI-助理人员如何在先前研究的菜单搜索任务中以实际操作的方式使用认知模型。