Centaurs are half-human, half-AI decision-makers where the AI's goal is to complement the human. To do so, the AI must be able to recognize the goals and constraints of the human and have the means to help them. We present a novel formulation of the interaction between the human and the AI as a sequential game where the agents are modelled using Bayesian best-response models. We show that in this case the AI's problem of helping bounded-rational humans make better decisions reduces to a Bayes-adaptive POMDP. In our simulated experiments, we consider an instantiation of our framework for humans who are subjectively optimistic about the AI's future behaviour. Our results show that when equipped with a model of the human, the AI can infer the human's bounds and nudge them towards better decisions. We discuss ways in which the machine can learn to improve upon its own limitations as well with the help of the human. We identify a novel trade-off for centaurs in partially observable tasks: for the AI's actions to be acceptable to the human, the machine must make sure their beliefs are sufficiently aligned, but aligning beliefs might be costly. We present a preliminary theoretical analysis of this trade-off and its dependence on task structure.

本文提出了一种新的人工智能决策模型——Centaurs，旨在辅助有限理性的人类做出更好的决策。基于Bayesian最佳反应模型，我们建立了一种序列博弈模型，使得机器能够识别人类的目标和约束，并对其进行帮助。通过模拟实验，我们发现当Centaurs具备对人类行为的预测和分析能力时，它能够推断出人类的局限，并引导其做出更好的决策。除此之外，我们也探究了AI-human interaction中的新型权衡问题。

基于最优反应贝叶斯强化学习的贝叶斯自适应POMDP应用于半人马