Graduate School of Informatics, Nagoya University, Nagoya, Japan.
Institute for Advanced Research, Nagoya University, Nagoya, Japan.
Elife. 2024 May 7;13:e85694. doi: 10.7554/eLife.85694.
Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings that collaborative hunting has also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, using computational multi-agent simulations based on deep reinforcement learning, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between states and actions related to distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our computational ecological results emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.
合作狩猎，即捕食者扮演不同且互补的角色来捕获猎物，传统上被认为是一种需要大脑发达并涉及高级认知的先进狩猎策略。然而，最近的发现表明，合作狩猎也存在于大脑较小的脊椎动物中，这使得之前的观点受到了挑战。在这里，我们使用基于深度强化学习的计算多主体模拟，证明了合作狩猎的决策不一定依赖于复杂的认知过程。我们发现，通过一种相对简单的决策过程，即根据先前经验形成的与距离相关的内部表示的状态和动作之间的映射，可以实现明显复杂的协调。此外，我们证实了捕食者的这种决策规则对人类控制的未知猎物具有鲁棒性。我们的计算生态学结果强调了合作狩猎可以在自然界中各种种内和种间的相互作用中出现，并为社会性的进化提供了新的见解。
