Stackelberg惩罚和防欺凌自动驾驶汽车
Stackelberg Punishment and Bully-Proofing Autonomous Vehicles
摘要：
重复博弈中的互利行为可以通过惩罚的威胁来实施，正如博弈论中著名的“民间定理”所揭示的那样。“然而，对一个球员来说，产生这些抑制因素是有代价的。在这项工作中，我们试图通过计算“Stackelberg惩罚”来最大限度地减少这种成本，在这种惩罚中，玩家选择一种行为，在假设其他玩家将采取最佳对策的情况下，充分惩罚其他玩家，同时最大化自己的得分。这个想法推广了Stackelberg均衡的概念。已知的用于计算Stackelberg均衡的有效算法可以适于有效地产生Stackelberg惩罚。我们展示了这个想法在一个涉及虚拟自动驾驶汽车和人类参与者的实验中的应用。我们发现，具有Stackelberg惩罚政策的自动驾驶汽车不鼓励人类驾驶员在需要社会谈判的驾驶场景中进行欺凌。
英文摘要：
Mutually beneficial behavior in repeated games can be enforced via the threat of punishment, as enshrined in game theory's well-known "folk theorem." There is a cost, however, to a player for generating these disincentives. In this work, we seek to minimize this cost by computing a "Stackelberg punishment," in which the player selects a behavior that sufficiently punishes the other player while maximizing its own score under the assumption that the other player will adopt a best response. This idea generalizes the concept of a Stackelberg equilibrium. Known efficient algorithms for computing a Stackelberg equilibrium can be adapted to efficiently produce a Stackelberg punishment. We demonstrate an application of this idea in an experiment involving a virtual autonomous vehicle and human participants. We find that a self-driving car with a Stackelberg punishment policy discourages human drivers from bullying in a driving scenario requiring social negotiation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
