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The pervasive uncertainty and dynamic nature of real-world environmentspresent significant challenges for the widespread implementation ofmachine-driven Intelligent Decision-Making (IDM) systems. Consequently, IDMshould possess the ability to continuously acquire new skills and effectivelygeneralize across a broad range of applications. The advancement of ArtificialGeneral Intelligence (AGI) that transcends task and application boundaries iscritical for enhancing IDM. Recent studies have extensively investigated theTransformer neural architecture as a foundational model for various tasks,including computer vision, natural language processing, and reinforcementlearning. We propose that a Foundation Decision Model (FDM) can be developed byformulating diverse decision-making tasks as sequence decoding tasks using theTransformer architecture, offering a promising solution for expanding IDMapplications in complex real-world situations. In this paper, we discuss theefficiency and generalization improvements offered by a foundation decisionmodel for IDM and explore its potential applications in multi-agent game AI,production scheduling, and robotics tasks. Lastly, we present a case studydemonstrating our FDM implementation, DigitalBrain (DB1) with 1.3 billionparameters, achieving human-level performance in 870 tasks, such as textgeneration, image captioning, video game playing, robotic control, andtraveling salesman problems. As a foundation decision model, DB1 represents aninitial step toward more autonomous and efficient real-world IDM applications.

On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective