On Realization of Intelligent Decision Making in the Real World: A Foundation Decision Model Perspective
The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems. Consequently, IDM should possess the ability to continuously acquire new skills and effectively genera...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-12-01
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| Series: | CAAI Artificial Intelligence Research |
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| Online Access: | https://www.sciopen.com/article/10.26599/AIR.2023.9150026 |
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| author | Ying Wen Ziyu Wan Ming Zhou Shufang Hou Zhe Cao Chenyang Le Jingxiao Chen Zheng Tian Weinan Zhang Jun Wang |
| author_facet | Ying Wen Ziyu Wan Ming Zhou Shufang Hou Zhe Cao Chenyang Le Jingxiao Chen Zheng Tian Weinan Zhang Jun Wang |
| author_sort | Ying Wen |
| collection | DOAJ |
| description | The pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems. Consequently, IDM should possess the ability to continuously acquire new skills and effectively generalize across a broad range of applications. The advancement of Artificial General Intelligence (AGI) that transcends task and application boundaries is critical for enhancing IDM. Recent studies have extensively investigated the Transformer neural architecture as a foundational model for various tasks, including computer vision, natural language processing, and reinforcement learning. We propose that a Foundation Decision Model (FDM) can be developed by formulating diverse decision-making tasks as sequence decoding tasks using the Transformer architecture, offering a promising solution for expanding IDM applications in complex real-world situations. In this paper, we discuss the efficiency and generalization improvements offered by a foundation decision model for IDM and explore its potential applications in multi-agent game AI, production scheduling, and robotics tasks. Lastly, we present a case study demonstrating our FDM implementation, DigitalBrain (DB1) with 1.3 billion parameters, achieving human-level performance in 870 tasks, such as text generation, image captioning, video game playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 represents an initial step toward more autonomous and efficient real-world IDM applications. |
| format | Article |
| id | doaj-art-d02f672bec0a4ad19659c9de252e31cb |
| institution | OA Journals |
| issn | 2097-194X |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | CAAI Artificial Intelligence Research |
| spelling | doaj-art-d02f672bec0a4ad19659c9de252e31cb2025-08-20T02:22:58ZengTsinghua University PressCAAI Artificial Intelligence Research2097-194X2023-12-012915002610.26599/AIR.2023.9150026On Realization of Intelligent Decision Making in the Real World: A Foundation Decision Model PerspectiveYing Wen0Ziyu Wan1Ming Zhou2Shufang Hou3Zhe Cao4Chenyang Le5Jingxiao Chen6Zheng Tian7Weinan Zhang8Jun Wang9SEIEE, Shanghai Jiao Tong University, Shanghai 200240, ChinaSEIEE, Shanghai Jiao Tong University, Shanghai 200240, ChinaSEIEE, Shanghai Jiao Tong University, Shanghai 200240, ChinaDigital Brain Laboratory, Shanghai 201306, ChinaSEIEE, Shanghai Jiao Tong University, Shanghai 200240, ChinaSEIEE, Shanghai Jiao Tong University, Shanghai 200240, ChinaSEIEE, Shanghai Jiao Tong University, Shanghai 200240, ChinaSchool of Creativity and Art, ShanghaiTech University, Shanghai 201210, ChinaSEIEE, Shanghai Jiao Tong University, Shanghai 200240, ChinaDigital Brain Laboratory, Shanghai 201306, ChinaThe pervasive uncertainty and dynamic nature of real-world environments present significant challenges for the widespread implementation of machine-driven Intelligent Decision-Making (IDM) systems. Consequently, IDM should possess the ability to continuously acquire new skills and effectively generalize across a broad range of applications. The advancement of Artificial General Intelligence (AGI) that transcends task and application boundaries is critical for enhancing IDM. Recent studies have extensively investigated the Transformer neural architecture as a foundational model for various tasks, including computer vision, natural language processing, and reinforcement learning. We propose that a Foundation Decision Model (FDM) can be developed by formulating diverse decision-making tasks as sequence decoding tasks using the Transformer architecture, offering a promising solution for expanding IDM applications in complex real-world situations. In this paper, we discuss the efficiency and generalization improvements offered by a foundation decision model for IDM and explore its potential applications in multi-agent game AI, production scheduling, and robotics tasks. Lastly, we present a case study demonstrating our FDM implementation, DigitalBrain (DB1) with 1.3 billion parameters, achieving human-level performance in 870 tasks, such as text generation, image captioning, video game playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 represents an initial step toward more autonomous and efficient real-world IDM applications.https://www.sciopen.com/article/10.26599/AIR.2023.9150026artificial intelligenceintelligent decision makingtransformerfoundation decision model |
| spellingShingle | Ying Wen Ziyu Wan Ming Zhou Shufang Hou Zhe Cao Chenyang Le Jingxiao Chen Zheng Tian Weinan Zhang Jun Wang On Realization of Intelligent Decision Making in the Real World: A Foundation Decision Model Perspective CAAI Artificial Intelligence Research artificial intelligence intelligent decision making transformer foundation decision model |
| title | On Realization of Intelligent Decision Making in the Real World: A Foundation Decision Model Perspective |
| title_full | On Realization of Intelligent Decision Making in the Real World: A Foundation Decision Model Perspective |
| title_fullStr | On Realization of Intelligent Decision Making in the Real World: A Foundation Decision Model Perspective |
| title_full_unstemmed | On Realization of Intelligent Decision Making in the Real World: A Foundation Decision Model Perspective |
| title_short | On Realization of Intelligent Decision Making in the Real World: A Foundation Decision Model Perspective |
| title_sort | on realization of intelligent decision making in the real world a foundation decision model perspective |
| topic | artificial intelligence intelligent decision making transformer foundation decision model |
| url | https://www.sciopen.com/article/10.26599/AIR.2023.9150026 |
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