首页> 外文期刊>International Journal of Artificial Intelligence Tools: Architectures, Languages, Algorithms >MODELLING ROBOTIC COGNITIVE MECHANISMS BY HIERARCHICAL COOPERATIVE COEVOLUTION
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MODELLING ROBOTIC COGNITIVE MECHANISMS BY HIERARCHICAL COOPERATIVE COEVOLUTION

机译:通过分层合作协同进化建模机器人认知机理

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Recently, many brain modelling efforts attempt to support cognitive abilities of artificial organisms. The present work introduces a computational framework to address brain modelling, emphasizing on the integrative performance of substructures. Specifically, we present an agent-based representation of brain areas, together with a hierarchical cooperative coevolutionary scheme, which is able to highlight both the speciality of brain areas and their cooperative performance. The inherent ability of coevolutionary methods to design cooperative partial structures supports the design of partial brain models and, at the same time, provides a consistent method to achieve their integration. As a result, the proposed approach proceeds in either an incremental or a compound mode. Furthermore, the performance of the model in lesion conditions is considered during the design process to enforce the reliability of the result. Implemented models are embedded in a robotic platform to support its behavioral capabilities.
机译:最近,许多大脑建模工作试图支持人造生物的认知能力。本工作介绍了用于解决大脑建模的计算框架,重点是子结构的集成性能。具体来说,我们提出了基于代理人的大脑区域表示法,并提出了分层合作协同进化方案,该方案能够突出大脑区域的特殊性及其合作绩效。协同进化方法设计协作部分结构的固有能力支持部分大脑模型的设计,同时提供了一种实现它们集成的一致方法。结果,提出的方法以增量或复合模式进行。此外,在设计过程中要考虑病变情况下模型的性能,以增强结果的可靠性。已实现的模型嵌入在机器人平台中以支持其行为功能。

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