在异质景观中模拟生物体的移动性：记忆能提高觅食的成功率吗？
Modeling the mobility of living organisms in heterogeneous landscapes: Does memory improve foraging success?
摘要：
由于最近的技术进步，现在可以以前所未有的精度长时间跟踪许多生物体在其栖息地的运动模式。越来越多的数据提供了了解动物如何移动和测试基本运动模型的可能性。长期以来，随机行走一直是对微生物的主要描述，也有助于理解大型动物的觅食行为。然而，大多数脊椎动物，特别是人类和其他灵长类动物，依赖于复杂的认知工具，如空间地图，情景记忆和旅行成本折扣。这些属性要求其他建模方法的流动性模式。我们提出了一个觅食框架，学习移动代理使用基于内存和随机步骤的组合。我们调查它是如何有利地利用资源在异构和不断变化的环境中使用内存。确定性和随机探索的一个适当的平衡被发现，以最大限度地提高觅食效率，并产生一个复杂的时空顺序的轨迹。基于这种方法，我们提出了一些工具来分析一般的非随机性质的流动性模式。
英文摘要：
Thanks to recent technological advances, it is now possible to track with an unprecedented precision and for long periods of time the movement patterns of many living organisms in their habitat. The increasing amount of data available on single trajectories offers the possibility of understanding how animals move and of testing basic movement models. Random walks have long represented the main description for micro-organisms and have also been useful to understand the foraging behaviour of large animals. Nevertheless, most vertebrates, in particular humans and other primates, rely on sophisticated cognitive tools such as spatial maps, episodic memory and travel cost discounting. These properties call for other modeling approaches of mobility patterns. We propose a foraging framework where a learning mobile agent uses a combination of memory-based and random steps. We investigate how advantageous it is to use memory for exploiting resources in heterogeneous and changing environments. An adequate balance of determinism and random exploration is found to maximize the foraging efficiency and to generate trajectories with an intricate spatio-temporal order. Based on this approach, we propose some tools for analysing the non-random nature of mobility patterns in general.
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