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Differentiable User Models
Alex Hämäläinen · Mustafa Mert Çelikok · Samuel Kaski

@
in Human in the Loop Learning (HiLL) Workshop at NeurIPS 2022 »

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.

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Author Information

Alex Hämäläinen (Aalto University)
Mustafa Mert Çelikok (Aalto University)
Samuel Kaski (Aalto University and University of Manchester)

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