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).
Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning
Saarland University（萨尔兰大学）；Deutsches Forschungszentrum für Künstliche Intelligenz（德国人工智能研究中心）；Télécom Paris（巴黎电信学院）；Max Planck Institute for Informatics（马克斯·普朗克 informatics 研究所）
Uncertainty-Aware Autonomous Vehicles: Predicting the Road Ahead
School of Engineering, Computing and Mathematics, Oxford Brookes University, UK（工程、计算与数学学院，奥克斯福德布鲁克斯大学，英国）；Autonomous Driving and Intelligent Transport, Oxford Brookes University, UK（自动驾驶与智能交通，奥克斯福德布鲁克斯大学，英国）；Cognitive Robotics, TU Delft, Netherlands（认知机器人学，代尔夫特理工大学，荷兰）
