Towards A Foundation of Comprehensive Intelligent Agents for Smart Cities


Towards A Foundation of Comprehensive Intelligent Agents for Smart Cities – The goal of this research is to extend the use of artificial intelligence for smart cities using intelligent agents. A humanoid robot has been demonstrated to be an effective example of intelligent agents. In this paper, we propose a multi-agent framework based on self-learning and agent-generated human-generated world data and then use machine translation to translate the world data for a real world robot. Based on Machine Translation (MT) techniques from human-generated information, we then use a mapping algorithm to synthesize a map of a world in a human-human relation. The mapping algorithm takes the world data as input and the world data as output. We also provide two additional data sources, the map of a robot and human environment, which we classify as autonomous and not. The goal of the proposed framework is to incorporate a collaborative approach to a robot in order to make a learning process sustainable. The proposed framework and the mapping technique are applied to the problem of autonomous robots and have been successfully applied to real life robots. We demonstrate the usefulness of the proposed framework for a simulated real-world traffic network traffic case.

This paper proposes a deep neural network for the object detection and object classification tasks. Our proposed model is trained with a novel approach, where two deep neural networks are learned to deal with various object classes. The training set used for object class learning is the same as the one used in object classification. Two network-based approach is then implemented to solve the object detection and classification problems. The proposed approach is evaluated using the publicly available dataset of the 2016 COCO 2015 Object Detection & Classification Task. The classification results showed a significant improvement in recognition performance in the two-class setting over similar approach when the object classification dataset. The proposed method outperforms other state-of-the-art methods in the object detection and classification datasets.

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Towards A Foundation of Comprehensive Intelligent Agents for Smart Cities

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    Constrained Deep Network-Based Hierarchical Decision Making for Learning Item Levels and Reward OrientationThis paper proposes a deep neural network for the object detection and object classification tasks. Our proposed model is trained with a novel approach, where two deep neural networks are learned to deal with various object classes. The training set used for object class learning is the same as the one used in object classification. Two network-based approach is then implemented to solve the object detection and classification problems. The proposed approach is evaluated using the publicly available dataset of the 2016 COCO 2015 Object Detection & Classification Task. The classification results showed a significant improvement in recognition performance in the two-class setting over similar approach when the object classification dataset. The proposed method outperforms other state-of-the-art methods in the object detection and classification datasets.


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