论文标题
使用感知的内部模拟预测预期的动作
Predicting the intended action using internal simulation of perception
论文作者
论文摘要
本文提出了一个体系结构，该体系结构可以通过内部模拟以动作模式向量表示的感知状态来预测意图。为此，联想性自组织神经网络（A-SOM）用于构建层次认知结构，以识别和模拟基于骨架的人类行为。在实验中，使用三个不同的3D动作数据集评估了所提出的体系结构在识别和预测动作方面的能力。基于本文的实验，应用由动作模式向量代表的内部模拟感知状态提高了所有实验中识别任务的性能。此外，感知的内部模拟解决了对感觉输入访问有限的问题，以及对连续感知序列的未来预测。使用自组织神经网络（SOM）将系统的性能与类似体系结构进行比较和讨论。
This article proposes an architecture, which allows the prediction of intention by internally simulating perceptual states represented by action pattern vectors. To this end, associative self-organising neural networks (A-SOM) is utilised to build a hierarchical cognitive architecture for recognition and simulation of the skeleton based human actions. The abilities of the proposed architecture in recognising and predicting actions is evaluated in experiments using three different datasets of 3D actions. Based on the experiments of this article, applying internally simulated perceptual states represented by action pattern vectors improves the performance of the recognition task in all experiments. Furthermore, internal simulation of perception addresses the problem of having limited access to the sensory input, and also the future prediction of the consecutive perceptual sequences. The performance of the system is compared and discussed with similar architecture using self-organizing neural networks (SOM).
