我什么时候应该有同情心？移情参数估计在多智能体交互中的应用
When Shall I Be Empathetic? The Utility of Empathetic Parameter Estimation in Multi-Agent Interactions
摘要：
人-机器人交互（HRI）可以建模为不完全信息的动态或微分博弈，其中每个代理拥有私人奖励参数。由于在寻找完美的贝叶斯均衡的这种游戏的公开挑战，现有的研究往往考虑近似的解决方案组成的参数估计和运动规划步骤，为了解耦的信念和物理动力学。在参数估计中，目前的方法通常假设机器人的奖励参数是已知的。我们认为，通过错误地调节这个假设，机器人执行人类参数的非移情估计，导致即使在最简单的交互中也会出现不期望的值。我们通过研究一个反应时间短的两车无控制交叉口的情况下，测试这一论点。结果表明，当两个代理人都是不知不觉的侵略性（或非侵略性），共情导致更有效的参数估计和更高的奖励值，这表明共情是必要的，当代理人的真实参数与他们的共同信念不匹配。因此，建议的估计和规划算法比现有的方法更强大，通过充分承认在HRI的信息不对称的性质。最后，我们介绍了所提出的算法的实时执行的值逼近技术。
英文摘要：
Human-robot interactions (HRI) can be modeled as dynamic or differential games with incomplete information, where each agent holds private reward parameters. Due to the open challenge in finding perfect Bayesian equilibria of such games, existing studies often consider approximated solutions composed of parameter estimation and motion planning steps, in order to decouple the belief and physical dynamics. In parameter estimation, current approaches often assume that the reward parameters of the robot are known by the humans. We argue that by falsely conditioning on this assumption, the robot performs non-empathetic estimation of the humans' parameters, leading to undesirable values even in the simplest interactions. We test this argument by studying a two-vehicle uncontrolled intersection case with short reaction time. Results show that when both agents are unknowingly aggressive (or non-aggressive), empathy leads to more effective parameter estimation and higher reward values, suggesting that empathy is necessary when the true parameters of agents mismatch with their common belief. The proposed estimation and planning algorithms are therefore more robust than the existing approaches, by fully acknowledging the nature of information asymmetry in HRI. Lastly, we introduce value approximation techniques for real-time execution of the proposed algorithms.
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