Becoming incrementally reactive: on-line learning of an evolving decision tree array for robot navigation
Becoming incrementally reactive: on-line learning of an evolving decision tree array for robot navigation
复制标题
变得渐进反应：在线学习用于机器人导航的不断发展的决策树阵列
DOI：
10.1017/s0263574799001319
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发表时间：
1999
期刊：
影响因子：
2.7
通讯作者：
I. Sillitoe
中科院分区：
文献类型：
--
作者：
G. H. Hamzei;D. Mulvaney;I. Sillitoe
本文提出了一种新的层次多层决策树表示反应式机器人导航知识。在这种表示中，感知空间被分解为反映环境的分层世界集，这些世界在本质上是同质的，并且在复杂性上以有序的方式变化。每个世界用于生成相应的决策树，该决策树是增量训练的。机器人的瞬时感知用于从决策树中选择适当的规则，并且规则激活序列形成完整的轨迹。保持知识复杂性的可管理性和可控性的能力是该技术的一个重要方面。
This paper proposes a novel hierarchical multi-layer decision tree for representing reactive robot navigation knowledge. In this representation, the perception space is decomposed into a hierarchical set of worlds reflecting environments which are homogeneous in nature and which vary in complexity in an ordered manner. Each world is used to produce a corresponding decision tree which is trained incrementally. The instantaneous perception of the robot is used to select an appropriate rule from the decision tree and a sequence of rule activations form the complete trajectory. The ability to keep the knowledge complexity manageable and under control is an important aspect of the technique.