Abstract
Internet of Things simulations play significant roles in the diverse kinds of activities in our daily lives and have been extensively researched. Creating and controlling virtual agents in three-dimensional Internet of Things simulations is a key technology for achieving realism in three-dimensional simulations. Given that traditional virtual agent-based approaches have limitations for realism, it is necessary to improve the realism of three-dimensional Internet of Things simulations. This article proposes a Q-Network-based motivation framework that applies a Q-Network to select motivations from desires and hierarchical task network planning to execute actions based on goals of the selected motivations. The desires are to be identified and calculated based on states. Selected motivations will be chosen to determine the goals that agents must achieve. In the experiments, the proposed framework achieved an average accuracy of up to 85.5% when the Q-Network-based motivation model was trained. To verify the Q-Network-based motivation framework, a traditional Q-learning is also applied in the three-dimensional virtual environment. Comparing the results of the two frameworks, the Q-Network-based motivation framework shows better results than those of traditional Q-learning, as the accuracy of the Q-Network-based motivation is higher by 15.58%. The proposed framework can be applied to the diverse kinds of Internet of Things systems such as a training autonomous vehicle. Moreover, the proposed framework can generate big data on animal behaviors for other training systems.
Highlights
Internet of Things (IoT) simulations have become important in many aspects of human life, not just in entertainment and in medicine, education, the military, and for training
International Journal of Distributed Sensor Networks algorithms for data analysis and IoT cases, including machine learning and deep learning, provide the ability to learn without programming
The desires were calculated based on states that contain the environment information
Summary
Internet of Things (IoT) simulations have become important in many aspects of human life, not just in entertainment and in medicine, education, the military, and for training. The IoT requires algorithms that can extract knowledge and learn in real-time from collected data resulting from various resources such as temperature, traffic, and health. International Journal of Distributed Sensor Networks algorithms for data analysis and IoT cases, including machine learning and deep learning, provide the ability to learn without programming. These algorithms can be categorized into supervised, unsupervised, and reinforcement learning algorithms.[1,2,3,4] Simulations have many advantages, such as the virtualization of hazardous environments, extending the safe training of agents through the facilitation of experience without risking harm to humans or animals. The decisions of virtual agents can be enhanced using a variety of approaches, such as planning, knowledge representation, fuzzy logic, and neural networks.[5,6,7]
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