Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics
Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics
复制标题
进化机器人行为特征的系统推导
DOI：
10.7551/978-0-262-32621-6-ch036
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
A. Christensen
中科院分区：
文献类型：
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作者：
Jorge C. Gomes;Pedro Mariano;A. Christensen
由行为多样性驱动的进化技术，如新奇搜索，在进化机器人中显示出巨大的潜力。这些技术依赖于预先指定的行为特征来估计个体之间的相似性。特征通常是基于实验者的直觉和对任务的了解，以一种特别的方式定义的。或者，使用基于代理的传感器效应器值的通用特征。在本文中，我们提出了一种新颖的方法，该方法基于对代理及其环境的正式描述，允许系统地推导进化机器人的行为特征。系统派生的行为特征（SDBCs）超越了一般特征，因为它们可以包含与代理的内部状态、环境特征以及它们之间的关系相关的任务特定特征。我们在三个模拟的集体机器人任务中评估了SDBCs的新颖性搜索。我们的研究结果表明，在解决方案质量和行为空间探索方面，sdbc产生了与任务特定特征相当的性能。
Evolutionary techniques driven by behavioural diversity, such as novelty search, have shown significant potential in evolutionary robotics. These techniques rely on priorly specified behaviour characterisations to estimate the similarity between individuals. Characterisations are typically defined in an ad hoc manner based on the experimenter's intuition and knowledge about the task. Alternatively, generic characterisations based on the sensor-effector values of the agents are used. In this paper, we propose a novel approach that allows for systematic derivation of behaviour characterisations for evolutionary robotics, based on a formal description of the agents and their environment. Systematically derived behaviour characterisations (SDBCs) go beyond generic characterisations in that they can contain task-specific features related to the internal state of the agents, environmental features, and relations between them. We evaluate SDBCs with novelty search in three simulated collective robotics tasks. Our results show that SDBCs yield a performance comparable to the task-specific characterisations, in terms of both solution quality and behaviour space exploration.