Evolution of Self-Organized Task Specialization in Robot Swarms.
Evolution of Self-Organized Task Specialization in Robot Swarms.
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DOI：
10.1371/journal.pcbi.1004273
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发表时间：
2015-08
影响因子：
4.3
通讯作者：
Wenseleers T
中科院分区：
文献类型：
--
作者：
Ferrante E;Turgut AE;Duéñez-Guzmán E;Dorigo M;Wenseleers T
劳动分裂在生物系统中无处不在，这在动物社会和多细胞生物中都观察到了各种形式的复杂任务专业化，尽管显然是适应性的。最近，进化的群体已成为一个出色的测试床，以研究协调的群体级别行为的演变研究涉及一种在昆虫社会中常见的劳动分裂形式，并被称为“任务分配”，其中必须按照不同的个人顺序执行两组任务。当被利用时，降低切换成本并提高组的净效率的功能，并且当旨在执行不同子任务的行为库时，最容易获得任务专家的最佳组合，可以作为预先适应的构建块获得不同的子任务。然而，我们还首次展示了自我组织的任务专业化可以完全从头开始进化，仅从基本的低级行为基原始人开始，使用一种自然启发的进化方法，称为语法进化。仅通过选择整体群体绩效来实现，而没有提供有关如何将全局对象检索任务分为较小的子任务的任何先前信息。大自然为发展复杂的社会性和任务专业化所走的可能道路。
许多生物系统通过将其分为更精细的子任务来执行，例如在蚂蚁，蜜蜂或术语等社会昆虫的高级劳动中看到。选择可能导致如此复杂的社会性回答这个问题，我们使用了模拟的机器人团队，并在觅食任务中人为地发展了最大的绩效。展示自组织的劳动力的机器人，在该机器人中，不同的机器人会自动专门执行小组中的不同子任务。将物品回到其基础上，可以分为较小的子任务。系统设法发展了复杂的社会性和分工。
Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behavior. Here we use this framework for the first time to study the evolutionary origin of behavioral task specialization among groups of identical robots. The scenario we study involves an advanced form of division of labor, common in insect societies and known as “task partitioning”, whereby two sets of tasks have to be carried out in sequence by different individuals. Our results show that task partitioning is favored whenever the environment has features that, when exploited, reduce switching costs and increase the net efficiency of the group, and that an optimal mix of task specialists is achieved most readily when the behavioral repertoires aimed at carrying out the different subtasks are available as pre-adapted building blocks. Nevertheless, we also show for the first time that self-organized task specialization could be evolved entirely from scratch, starting only from basic, low-level behavioral primitives, using a nature-inspired evolutionary method known as Grammatical Evolution. Remarkably, division of labor was achieved merely by selecting on overall group performance, and without providing any prior information on how the global object retrieval task was best divided into smaller subtasks. We discuss the potential of our method for engineering adaptively behaving robot swarms and interpret our results in relation to the likely path that nature took to evolve complex sociality and task specialization. Many biological systems execute tasks by dividing them into finer sub-tasks first. This is seen for example in the advanced division of labor of social insects like ants, bees or termites. One of the unsolved mysteries in biology is how a blind process of Darwinian selection could have led to such highly complex forms of sociality. To answer this question, we used simulated teams of robots and artificially evolved them to achieve maximum performance in a foraging task. We find that, as in social insects, this favored controllers that caused the robots to display a self-organized division of labor in which the different robots automatically specialized into carrying out different subtasks in the group. Remarkably, such a division of labor could be achieved even if the robots were not told beforehand how the global task of retrieving items back to their base could best be divided into smaller subtasks. This is the first time that a self-organized division of labor mechanism could be evolved entirely de-novo. In addition, these findings shed significant new light on the question of how natural systems managed to evolve complex sociality and division of labor.