A Survey on Multi-Agent Communication

A Survey on Multi-Agent Communication – We propose a general framework for automatic collaborative decision support by designing a reinforcement learning framework specifically designed for automated decision support. The learning-based policy search model enables a human observer to identify a problem-relevant decision and predict the best policy. We use our framework to design an approach to collaborative strategy evaluation with a variety of policy search algorithms. The framework learns to optimize an objective function through a reinforcement learning algorithm that exploits the reward function to make decisions. The reinforcement learning algorithm obtains the optimal policy that maximizes its expected payoff over the current policy’s reward. We demonstrate the effectiveness of the framework by using it as an example to demonstrate the effectiveness of collaborative policy evaluation.

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.

A Framework for Identifying and Mining Object Specific Instances from Compressed Video Frames

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A Survey on Multi-Agent Communication

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  • Learning Data Representations for Video Classification with Convolutional Neural Networks

    Towards A Foundation of Comprehensive Intelligent Agents for Smart CitiesThe 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.


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