Evolution of Collective Behaviour in an Artificial World using Linguistic Fuzzy Rule-Based Systems

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.集体行为（Collective behaviour）是一种引人入胜且易于观测的现象，吸引了诸多领域研究者的关注。在生物学领域，计算模型已被广泛用于探究集体行为的各类属性，例如：群体内信息传递、集群优势（反捕食、觅食）、群体决策过程以及集群行为类型。然而，“为何会演化出此类行为”这一问题迄今尚未得到充分解答。本文聚焦于驱动此类行为演化的选择压力，而演化计算模型（evolutionary computational models）已被用于验证各类生物学假说。此类模型大多采用遗传算法对已有的非演化模型的参数进行调优，但极少有研究尝试从零开始演化集体行为。在这些少数尝试中，成功的案例均展现出集群或群集行为。实验证据表明，鱼群存在三类行为模式：群集（swarming）、旋转集群（milling）与极化集群（polarized）。本文提出了一种全新的类人工生命演化模型，其中个体AI智能体（AI Agent）由基于语言型模糊规则的系统所支配，该模型能够演化出上述三类行为模式。