Towards a Cognitive System that Can Recognize Spatial Regions Based on Context
Towards a Cognitive System that Can Recognize Spatial Regions Based on Context
复制标题
建立一个可以根据上下文识别空间区域的认知系统
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
John D. Kelleher
中科院分区：
文献类型：
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作者：
Nick Hawes;M. Klenk;Kate Lockwood;Graham S. Horn;John D. Kelleher
为了与真实的世界中的人协作，认知系统必须能够表示和推理人类环境中的空间区域。考虑命令“走到教室前面”。所提到的空间区域（教室的前面）是不能单独使用几何感知的。相反，它是由其功能用途定义的，由附近的物体及其配置暗示。在本文中，我们将这些领域定义为上下文相关的空间区域，并提出了一个认知系统，能够通过结合定性空间表示，语义标签和类比来学习它们。该系统能够生成一组定性空间表示，描述其在世界中感知到的实体的配置。然后，它可以被教导上下文相关的空间区域使用锚点定义在这些表示。从这一点，然后我们演示了如何现有的计算模型的类比可以用来检测上下文相关的空间区域在以前看不见的房间。为了评估这一过程，我们将检测到的区域与人类志愿者在真实的房间地图上做出的注释进行比较。
In order to collaborate with people in the real world, cognitive systems must be able to represent and reason about spatial regions in human environments. Consider the command "go to the front of the classroom". The spatial region mentioned (the front of the classroom) is not perceivable using geometry alone. Instead it is defined by its functional use, implied by nearby objects and their configuration. In this paper, we define such areas as context-dependent spatial regions and present a cognitive system able to learn them by combining qualitative spatial representations, semantic labels, and analogy. The system is capable of generating a collection of qualitative spatial representations describing the configuration of the entities it perceives in the world. It can then be taught context-dependent spatial regions using anchor pointsdefined on these representations. From this we then demonstrate how an existing computational model of analogy can be used to detect context-dependent spatial regions in previously unseen rooms. To evaluate this process we compare detected regions to annotations made on maps of real rooms by human volunteers.