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Knowledge Acquisition and Design Using Semantics and Perception: A Case Study for Autonomous Robots
Neural Processing Letters ( IF 2.6 ) Pub Date : 2020-07-27 , DOI: 10.1007/s11063-020-10311-x
Cristiano Russo , Kurosh Madani , Antonio M. Rinaldi

The pervasive use of artificial intelligence and neural networks in several different research fields has noticeably improved multiple aspects of human life. The application of these techniques to machines has made them progressively more “intelligent” and able to solve tasks considered extremely complex for a human being. This technological evolution has deeply influenced the way we interact with machines. Purely symbolic artificial intelligence and techniques like ontologies, have also been successfully used in the past applied to robotics, but have also shown some limitations and failings in the knowledge construction task. In fact, the exhibited “intelligence” is rarely the result of a real autonomous decision, but it is rather hard-encoded in the machine. While a number of approaches have already been proposed in literature concerning knowledge acquisition from the surrounding environment, they are either exclusively based on low-level features or they involve solely high-level semantics-based attributes. Moreover, they often don’t use a general high-level knowledge base for grounding the acquired knowledge. In this contexts, the use of semantics technologies, such as ontologies, is mostly employed for action-oriented tasks. In this article we propose an extension of a novel approach for knowledge acquisition based on a general semantic knowledge-base and the fusion of semantics and visual information by means of neural networks and ontologies. The proposed approach has been implemented on a humanoid robotic platform and the experimental results are shown and discussed.



中文翻译:

基于语义和知觉的知识获取和设计:以自主机器人为例

人工智能和神经网络在几个不同的研究领域中的广泛使用已显着改善了人类生活的多个方面。这些技术在机器上的应用使它们逐渐变得更加“智能”,并能够解决人们认为极为复杂的任务。这种技术发展深刻地影响了我们与机器交互的方式。纯粹的符号人工智能和技术(例如本体论)过去也已成功应用于机器人技术,但在知识构建任务中也显示出一些局限性和缺陷。实际上,所展示的“智能”很少是真正自主决定的结果,而是在机器中很难编码的。尽管在文献中已经提出了许多关于从周围环境中获取知识的方法,但是它们要么完全基于低级功能,要么仅涉及基于高级语义的属性。而且,他们通常不使用一般的高级知识库来获得所获得的知识。在这种情况下,语义技术(例如本体)的使用主要用于面向动作的任务。在本文中,我们提出了一种基于通用语义知识库的知识获取新方法的扩展,并借助神经网络和本体将语义和视觉信息融合在一起。所提出的方法已在类人机器人平台上实现,并显示和讨论了实验结果。它们要么仅基于低级功能,要么仅包含基于高级语义的属性。而且,他们通常不使用一般的高级知识库来获得所获得的知识。在这种情况下,语义技术(例如本体)的使用主要用于面向动作的任务。在本文中,我们提出了一种基于通用语义知识库的知识获取新方法的扩展,并借助神经网络和本体将语义和视觉信息融合在一起。所提出的方法已在类人机器人平台上实现,并显示和讨论了实验结果。它们要么仅基于低级功能,要么仅包含基于高级语义的属性。而且,他们通常不使用一般的高级知识库来获得所获得的知识。在这种情况下,语义技术(例如本体)的使用主要用于面向动作的任务。在本文中,我们提出了一种基于通用语义知识库的知识获取新方法的扩展,并借助神经网络和本体将语义和视觉信息融合在一起。所提出的方法已在类人机器人平台上实现,并显示和讨论了实验结果。诸如本体之类的语义技术的使用主要用于面向行动的任务。在本文中,我们提出了一种基于通用语义知识库的知识获取新方法的扩展,并借助神经网络和本体将语义和视觉信息融合在一起。所提出的方法已在类人机器人平台上实现,并显示和讨论了实验结果。诸如本体之类的语义技术的使用主要用于面向行动的任务。在本文中,我们提出了一种基于通用语义知识库的知识获取新方法的扩展,并借助神经网络和本体将语义和视觉信息融合在一起。所提出的方法已在类人机器人平台上实现,并显示和讨论了实验结果。

更新日期:2020-07-27
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