Demšar Jure, Lebar Bajec Iztok
Faculty of Computer and Information Science, Večna Pot 113, 1000 Ljubljana, Slovenia.
PLoS One. 2017 Jan 3;12(1):e0168876. doi: 10.1371/journal.pone.0168876. eCollection 2017.
Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question 'why,' however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarming, milling and polarized. In this paper we present a novel, artificial life-like evolutionary model, where individual agents are governed by linguistic fuzzy rule-based systems, which is capable of evolving all three classes of behaviour.
群体行为是一种引人入胜且易于观察的现象，吸引了众多研究人员。在生物学中，计算模型已被广泛用于研究群体行为的各种特性，例如：群体内信息传递、群体的益处（抵御捕食、觅食）、群体决策过程以及群体行为类型。然而，“为什么”这个问题在很大程度上仍未得到解答。这里关注的是哪些压力导致了这种行为的进化，并且进化计算模型已经被用于检验各种生物学假设。这些模型大多使用遗传算法来调整先前提出的非进化模型的参数，但很少有尝试从零开始进化群体行为的。在这些尝试中，成功的案例展现出聚集或蜂拥行为。经验证据表明，在鱼群中存在三类行为：蜂拥、乱转和极化。在本文中，我们提出了一种新颖的、类似人工生命的进化模型，其中个体智能体由基于语言模糊规则的系统控制，该模型能够进化出所有这三类行为。
