Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulations
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-07-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719866385 |
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| _version_ | 1850176716316606464 |
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| author | Hieu Trong Nguyen Phuong Minh Chu Jisun Park Yunsick Sung Kyungeun Cho |
| author_facet | Hieu Trong Nguyen Phuong Minh Chu Jisun Park Yunsick Sung Kyungeun Cho |
| author_sort | Hieu Trong Nguyen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-bdaf20a56fe34e60bfc117fc99c0ea64 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-bdaf20a56fe34e60bfc117fc99c0ea642025-08-20T02:19:12ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-07-011510.1177/1550147719866385Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulationsHieu Trong NguyenPhuong Minh ChuJisun ParkYunsick SungKyungeun ChoInternet 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.https://doi.org/10.1177/1550147719866385 |
| spellingShingle | Hieu Trong Nguyen Phuong Minh Chu Jisun Park Yunsick Sung Kyungeun Cho Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulations International Journal of Distributed Sensor Networks |
| title | Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulations |
| title_full | Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulations |
| title_fullStr | Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulations |
| title_full_unstemmed | Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulations |
| title_short | Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulations |
| title_sort | intelligent motivation framework based on q network for multiple agents in internet of things simulations |
| url | https://doi.org/10.1177/1550147719866385 |
| work_keys_str_mv | AT hieutrongnguyen intelligentmotivationframeworkbasedonqnetworkformultipleagentsininternetofthingssimulations AT phuongminhchu intelligentmotivationframeworkbasedonqnetworkformultipleagentsininternetofthingssimulations AT jisunpark intelligentmotivationframeworkbasedonqnetworkformultipleagentsininternetofthingssimulations AT yunsicksung intelligentmotivationframeworkbasedonqnetworkformultipleagentsininternetofthingssimulations AT kyungeuncho intelligentmotivationframeworkbasedonqnetworkformultipleagentsininternetofthingssimulations |