Intelligent decision making optimization of rolling mills through data prediction

Steel rolling mills have complex processes,specifications,and varieties,along with certain process quality fluctuations and complex production events,making production management decisions difficult.With the development of industrial big data technology,several industrial event solutions based on data have been proposed.These solutions are supported by predictive data and remarkably improve the production level.Taking a heavy plate production line as the research object,through scientific calculations based on historical big data,this paper establishes an optimization logic for plan arrangement,forecasts the quality through the stable relationship between data and quality,intelligently optimizes the subsequent process flow,improves the production line capacity,and reduces the process bottlenecks.

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 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.

Modeling heterogeneous behaviors with different strategies in a terrorist attack

Existing simulations of terrorist attacks do not consider individual variations.To overcome this lim-itation,we propose a framework to model heterogeneous behavior of individuals during terrorist attacks.We constructed an emotional model that integrated personality and visual perception for pedestrians.The emotional model was then integrated with pedestrian relationship networks to establish a decision-making model that sup-ported pedestrians’altruistic behaviors.A mapping model has been developed to correlate antisocial personality traits with attack strategies employed by terrorists.Experiments demonstrate that the proposed algorithm can generate practical heterogeneous behaviors that align with existing psychological research findings.

AI in Human-computer Gaming:Techniques,Challenges and Opportunities

With the breakthrough of AlphaGo,human-computer gaming AI has ushered in a big explosion,attracting more and more researchers all over the world.As a recognized standard for testing artificial intelligence,various human-computer gaming AI systems(AIs)have been developed,such as Libratus,OpenAI Five,and AlphaStar,which beat professional human players.The rapid development of human-computer gaming AIs indicates a big step for decision-making intelligence,and it seems that current techniques can handle very complex human-computer games.So,one natural question arises:What are the possible challenges of current techniques in human-computer gaming and what are the future trends?To answer the above question,in this paper,we survey recent successful game AIs,covering board game AIs,card game AIs,first-person shooting game AIs,and real-time strategy game AIs.Through this survey,we 1)compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human-level AIs;2)summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer games;3)raise the challenges or drawbacks of current techniques in the successful AIs;and 4)try to point out future trends in human-computer gaming AIs.Finally,we hope that this brief review can provide an introduction for beginners and inspire insight for researchers in the field of AI in human-computer gaming.