论文标题
不断发展的合作身体任务的二元策略
Evolving Dyadic Strategies for a Cooperative Physical Task
论文作者
论文摘要
许多合作的身体任务要求个人扮演专业角色（例如，领导者追随者）。人类是熟练的合作者，在天生的角色之间谈判这些角色和过渡。然而，如何委派和重新分配角色尚未得到很好的理解。使用遗传算法，我们进化了模拟药物来探索可行的角色转换策略的空间。在合作手动任务中应用这些切换策略，代理处理视觉和触觉提示，以决定何时切换角色。然后，我们分析与合作相关的属性进化的虚拟人群：负载共享和时间协调。我们发现表现最佳的二元组表现出高度的时间协调（反同步）。反过来，反同步与合作剂参数之间的对称性相关。这些模拟提供了关于人类合作者如何在二元任务中介绍角色的假设。
Many cooperative physical tasks require that individuals play specialized roles (e.g., leader-follower). Humans are adept cooperators, negotiating these roles and transitions between roles innately. Yet how roles are delegated and reassigned is not well understood. Using a genetic algorithm, we evolve simulated agents to explore a space of feasible role-switching policies. Applying these switching policies in a cooperative manual task, agents process visual and haptic cues to decide when to switch roles. We then analyze the evolved virtual population for attributes typically associated with cooperation: load sharing and temporal coordination. We find that the best performing dyads exhibit high temporal coordination (anti-synchrony). And in turn, anti-synchrony is correlated to symmetry between the parameters of the cooperative agents. These simulations furnish hypotheses as to how human cooperators might mediate roles in dyadic tasks.
