In recent years,the new-style tea-drinking market has expanded rapidly.With the upgrading of consumer demands and the younger generation becoming the primary consumer group,the space experience centered around the“th...In recent years,the new-style tea-drinking market has expanded rapidly.With the upgrading of consumer demands and the younger generation becoming the primary consumer group,the space experience centered around the“third space”has become a crucial strategy for brands to differentiate themselves.This research focuses on the impact mechanism of spatial scenario design on the brand value of tea-drinking brands,aiming to explore the internal relationships among the key elements of spatial design,brand perception,consumers’emotional connection,and consumption willingness,providing theoretical support and practical references for scenario-based design in the industry.Through a combination of literature research and case-analysis methods,this study systematically reviews relevant domestic and international research on scenario-based design and brand value over the past five years.It selects representative brands as cases,deeply analyzes their spatial design strategies,user feedback,and market performance,and summarizes both successful experiences and existing problems.Scenario-based design is an important means to enhance the brand value of tea-drinking brands,but it needs to follow the four-in-one design principle of“brand consistency,functional diversity,experience coherence,and cost controllability.”In the future,brands should focus on the in-depth exploration and innovative expression of cultural elements,strengthen the multi-functional attributes of spaces,and achieve seamless integration of online and offline scenarios through digital means.In addition,it is recommended to adopt modular design to reduce scenario-updating costs and increase the return on investment.This research provides a theoretical basis and practical path for the optimization of the spatial design of tea-drinking brands,and has important reference value for promoting the high-quality development of the industry.展开更多
Objective:To evaluate the efficacy of scenario-based participatory teaching methods in thoracic surgery nursing education.Methods:Sixty undergraduate nursing students were randomly assigned to two groups:a traditional...Objective:To evaluate the efficacy of scenario-based participatory teaching methods in thoracic surgery nursing education.Methods:Sixty undergraduate nursing students were randomly assigned to two groups:a traditional teaching group and a scenario-based participatory teaching group,with 30 students each.The teaching outcomes of both groups were assessed.Results:The clinical reasoning assessment scores of the scenario-based participatory teaching group were significantly higher than those of the traditional group(P<0.05).Additionally,the scenario group demonstrated higher satisfaction levels,superior theoretical and practical skills,improved patient education effectiveness during admission and discharge,and enhanced emergency response coordination(P<0.05).Conclusion:Scenario-based participatory teaching effectively enhances the comprehensive competencies of nursing students in thoracic surgery,demonstrating favorable educational outcomes.展开更多
In a first for the African continent,Senegal will host the Dakar 2026 Youth Olympic Games(YOG)from 31 October to 13 November.The Dakar 2026 YOG carry a strong symbolic ambition,embodied by their motto“Africa welcomes...In a first for the African continent,Senegal will host the Dakar 2026 Youth Olympic Games(YOG)from 31 October to 13 November.The Dakar 2026 YOG carry a strong symbolic ambition,embodied by their motto“Africa welcomes,Dakar celebrates.”Host Senegal sees the event as a catalyst for its influence,the modernisation of its infrastructure,and the mobilisation of its youth.展开更多
GameQualityAssurance(QA)currently relies heavily onmanual testing,a process that is both costly and time-consuming.Traditional script-and log-based automation tools are limited in their ability to detect unpredictable...GameQualityAssurance(QA)currently relies heavily onmanual testing,a process that is both costly and time-consuming.Traditional script-and log-based automation tools are limited in their ability to detect unpredictable visual bugs,especially those that are context-dependent or graphical in nature.As a result,many issues go unnoticed during manual QA,which reduces overall game quality,degrades the user experience,and creates inefficiencies throughout the development cycle.This study proposes two approaches to address these challenges.The first leverages a Large Language Model(LLM)to directly analyze gameplay videos,detect visual bugs,and automatically generate QA reports in natural language.The second approach introduces a pipeline method:first generating textual descriptions of visual bugs in game videos using the ClipCap model,then using those descriptions as input for the LLM to synthesize QA reports.Through these two multi-faceted approaches,this study evaluates the feasibility of automated game QA systems.To implement this system,we constructed a visual bug database derived from real-world game cases and fine-tuned the ClipCap model for the game video domain.Our proposed approach aims to enhance both efficiency and quality in game development by reducing the burden of manual QA while improving the accuracy of visual bug detection and ensuring consistent,reliable report generation.展开更多
The problem of maneuvering for a servicing spacecraft(inspector)to inspect a noncooperative spacecraft(evader)in cislunar space is investigated in this paper.The evader,which may be a malfunctioning or uncontrolled sa...The problem of maneuvering for a servicing spacecraft(inspector)to inspect a noncooperative spacecraft(evader)in cislunar space is investigated in this paper.The evader,which may be a malfunctioning or uncontrolled satellite,introduces uncertainties due to its potential maneuvering capabilities.To address this challenge,the scenario is modeled as a special orbital game,incorporating the unique complexities of the cislunar environment.A variable-duration,turn-based inspection and anti-inspection game model is designed.The model defines both players'rules,constraints,and victory conditions,providing a framework for non-cooperative inspection.Strategies for both players are developed and validated based on their dynamical properties.The inspector's strategy integrates two-body Lambert transfers with shooting methods,while the evader's strategy aims to maximize the inspector's fuel consumption.Simulation results show that the evader's optimal strategy involves deliberate fluctuations in its lunar periapsis altitude,with the inspector's requiredΔV up to eight times greater than the evader's.The impact of game constraints is evaluated,and the effectiveness of deploying the inspector in low lunar orbit is compared with the inspector at the Earth-Moon Lagrange point L1.The strengths and weaknesses of both are shown.These findings provide valuable insights for future orbital servicing and orbital games.展开更多
In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task exec...In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.展开更多
Vaccination is a key strategy to curb the spread of epidemics.Heterologous vaccination,unlike homologous vaccination which acts on a single target and forms a single immune barrier,covers multiple targets for broader ...Vaccination is a key strategy to curb the spread of epidemics.Heterologous vaccination,unlike homologous vaccination which acts on a single target and forms a single immune barrier,covers multiple targets for broader protection.Yet,heterologous vaccination involves a complex decision process that conventional game-theoretic approaches,such as classical,evolutionary,and minority games cannot adequately capture.The parallel minority game(PMG)can handle bounded-rational,multi-choice decisions,but its application in vaccine research remains rare.In this study,we propose a vaccination-transmission coupled dynamic mechanism based on the parallel minority game and simulate it on a two-dimensional lattice.Using actual observational data and a mean-field mathematical model,we verify the effectiveness of this mechanism in simulating realistic vaccination behavior and transmission dynamics.We further analyze the impact of key parameters,such as vaccine efficacy differences and the proportion of individuals eligible for vaccine switching,on containment effectiveness.Our results demonstrate that heterologous vaccination surpasses homologous vaccination in containment effectiveness,particularly when vaccine efficacy varies significantly.This work provides a novel framework and empirical evidence for understanding individual decision-making and population-wide immunity formation in multi-vaccine settings.展开更多
An attack-resilient distributed Nash equilibrium(NE) seeking problem is addressed for noncooperative games of networked systems under malicious cyber-attacks,i.e.,false data injection(FDI) attacks.Different from many ...An attack-resilient distributed Nash equilibrium(NE) seeking problem is addressed for noncooperative games of networked systems under malicious cyber-attacks,i.e.,false data injection(FDI) attacks.Different from many existing distributed NE seeking works,it is practical and challenging to get resilient adaptively distributed NE seeking under unknown and unbounded FDI attacks.An attack-resilient NE seeking algorithm that is distributed(i.e.,independent of global information on the graph's algebraic connectivity,Lipschitz and monotone constants of pseudo-gradients,or number of players),is presented by means of incorporating the consensus-based gradient play with a distributed attack identifier so as to achieve simultaneous NE seeking and attack identification asymptotically.Another key characteristic is that FDI attacks are allowed to be unknown and unbounded.By exploiting nonsmooth analysis and stability theory,the global asymptotic convergence of the developed algorithm to the NE is ensured.Moreover,we extend this design to further consider the attack-resilient NE seeking of double-integrator players.Lastly,numerical simulation and practical experiment results are presented to validate the developed algorithms' effectiveness.展开更多
This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants(VPP).A generalized credibility-based fuzzy chance constraint...This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants(VPP).A generalized credibility-based fuzzy chance constraint programming approach is adopted to address uncertainties stemming from renewable generation and load demand within individual VPPs,while robust optimization techniques manage electricity and thermal price volatilities.Building upon this foundation,a hierarchical Nash-Stackelberg game model is established across multiple VPPs.Within each VPP,a Stackelberg game resolves the strategic interaction between the operator and photovoltaic prosumers(PVP).Among VPPs,a cooperative Nash bargaining model coordinates alliance formation.The problem is decomposed into two subproblems:maximizing coalitional benefits,and allocating cooperative surpluses via payment bargaining,solved distributively using the alternating direction method of multipliers(ADMM).Case studies demonstrate that the proposed strategy significantly enhances the economic efficiency and uncertainty resilience of multi-VPP alliances.展开更多
To uncover the decision-making mechanisms and evolutionary dynamics of multiple stakeholders in highway noise pollution control,a three-party evolutionary game model involving the government,operators,and the public i...To uncover the decision-making mechanisms and evolutionary dynamics of multiple stakeholders in highway noise pollution control,a three-party evolutionary game model involving the government,operators,and the public is constructed.The operation period is divided into different stages for differentiated analysis.A simulation analysis was performed on the Lituo sinking section of the Beijing-Hong Kong-Macao Highway to assess the impact of variations in critical elements on the system.The results indicate that the Lituo sinking section of the Beijing-Hong Kong-Macao Highway is currently in its early stage of development,with the corresponding strategies being active regulation,excessive emissions,and supervision.When the cost of the government’s active regulation decreases from 1×10^(5) to 5×10^(4) yuan,the system converges more rapidly toward the active regulation strategy.When the cost of the operator’s excessive emissions increases from 14.08×10^(6) to 20.00×10^(6) yuan,the system drives the operator toward the standardized emission strategy.In addition,when the cost of public supervision decreases from 15×10^(4) to 5×10^(4) yuan and the compensation paid by operators to the public increases from 1.288×10^(6) to 2.576×10^(6) yuan,the system converges more quickly toward the supervision strategy.The cost of the operator’s excessive emissions serves as the core decision variable for achieving the ideal equilibrium in the three-party game involving government active regulation,operator standardized emissions,and public supervision.展开更多
In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dyna...In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dynamic fighting scenarios,and exists situations where the scenario and rule of the Colonel Blotto(CB)game are too restrictive in real world.To address these issues,a support stage is added as supplementary for pre-allocated results,in which a novel two-stage competitive resource assignment problem is formulated based on CB game and stochastic Lanchester equation(SLE).Further,the force attrition in these two stages is formulated as a stochastic progress to consider the complex fighting progress,including the case that the player with fewer resources defeats the player with more resources and wins the battlefield.For solving this two-stage resource assignment problem,nested solving and no-regret learning are proposed to search the optimal resource assignment strategies.Numerical experiments are taken to analyze the effectiveness of the proposed model and study the assignment strategies in various cases.展开更多
This paper studies an indefinite mean-field game with Markov jump parameters,where all agents'diffusion terms depend on control variables and both state and control average terms(x.^((N)),u.^((N)))are considered.O...This paper studies an indefinite mean-field game with Markov jump parameters,where all agents'diffusion terms depend on control variables and both state and control average terms(x.^((N)),u.^((N)))are considered.One notable aspect is the relaxation of the assumption regarding the positivity or non-negativity of weight matrices within costs,allowing for zero or even negative values.By virtue of mean-field methods and decomposition techniques,we have derived decentralized strategies presented by Hamiltonian systems and a new type of consistency condition system.These systems consist of fully coupled regime-switching forward-backward stochastic differential equations that do not conform to the Monotonicity condition.The well-posedness of these strategies is established by employing a relaxed compensator method with an easily verifiable Condition(RC)and the decomposition technique.Furthermore,we demonstrate that the resulting decentralized strategies achieve anϵ-Nash equilibrium in the indefinite case without any assumptions on admissible control sets using novel estimates of the disturbed state and cost function.Finally,our theoretical results are applied to resolve a class of mean-variance portfolio selection problems.We provide corresponding numerical simulation results and economic explanations.展开更多
Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over p...Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution.展开更多
The decisions concerning portfolio selection for army engineering and manufacturing development projects determine the benefit of those projects to the country concerned.Projects are typically selected based on ex ant...The decisions concerning portfolio selection for army engineering and manufacturing development projects determine the benefit of those projects to the country concerned.Projects are typically selected based on ex ante estimates of future return values,which are usually difficult to specify or only generated after project launch.A scenario-based approach is presented here to address the problem of selecting a project portfolio under incomplete scenario information and interdependency constraints.In the first stage,the relevant dominance concepts of scenario analysis are studied to handle the incomplete information.Then,a scenario-based programming approach is proposed to handle the interdependencies to obtain the projects,whose return values are multi-criteria with interval data.Finally,an illustrative example of army engineering and manufacturing development shows the feasibility and advantages of the scenario-based multi-objective programming approach.展开更多
In software engineering, a scenario describes an anticipated usage of a software system. As scenarios are useful to understand the requirements and functionalities of a software system, the scenario-based analysis is ...In software engineering, a scenario describes an anticipated usage of a software system. As scenarios are useful to understand the requirements and functionalities of a software system, the scenario-based analysis is widely used in various tasks, especially in the design stage of software architectures. Although researchers have proposed various scenario-based approaches to analyse software architecture, there are still limitations in this research field, and a key limitation is that scenarios are typically not formally defined and thus may contain ambiguities. As these ambiguities may lead to defects, it is desirable to reduce them as many as possible. In order to reduce ambiguity in scenario-based software architecture analysis, this paper introduces a creative computing approach to scenario-based software requirements analysis. Our work expands this idea in three directions. Firstly, we extend an architecture description language(ADL)-based language – Breeze/ADL to model the software architecture. Secondly, we use a creative rule – combinational rule(CR) to combine the vector clock algorithm for reducing the ambiguities in modelling scenarios. Then, another creative rule – transformational rule(TR) is employed to help to transform our Breeze/ADL model to a popular model – unified modelling language(UML) model. We implement our approach as a plugin of Breeze, and illustrate a running example of modelling a poetry to music system in our case study.Our results show the proposed creative approach is able to reduce ambiguities of the software architecture in practice.展开更多
Lots of pervasive computing researchers are working on how to realize the user-centered intelligent pervasive computing environment as Mark Weiser figured out.Task abstraction is the fundamentation of configuration fo...Lots of pervasive computing researchers are working on how to realize the user-centered intelligent pervasive computing environment as Mark Weiser figured out.Task abstraction is the fundamentation of configuration for pervasive application.Based on task-oriented and descriptive properties of scenario,a scenario-based participatory design model was proposed to realize the task abstraction.The design model provided users and domain experts a useful mechanism to build the customized applications by separating system model into domain model and design model.In this design model,domain experts,together with users,stakeholders focus on the logic rules(domain model)and programmers work on the implementation(design model).In order to formalize the model description,a human-agent interaction language to transform users' goals and domain rules into executable scenarios was also discussed.An agent platform-describer used to link design and implementation of scenarios was developed to realize the configuration of applications according to different requirements.The demand bus application showed the design process and the usability of this model.展开更多
Motivated by a critical issue of airline planning process,this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruption...Motivated by a critical issue of airline planning process,this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruptions.Following the route network scheme and generated flight timetables,aircraft maintenance routing and crew scheduling are critical factors in airline planning and operations cost management.This study considers the simultaneous assignment of aircraft fleet and crew to the scheduled flight while satisfying a set of operational constraints,rules,and regulations.Considering multiple locations for airline maintenance and crew bases,we solve the problem of integrated Aircraft Maintenance Routing and Crew Rostering(AMRCR)to achieve the minimum airline cost.One real challenge to the efficiency of the planning results is the possible disruptions in the initial scheduled flights.Due to the fact that disruption scenarios are expressed discretely with a specified probability,and we provide adjustable decisions under disruption to deal with this disruption risk,we provide a Two-Stage Scenario-Based Robust Optimization(TSRO)model.In this model,here-and-now or first-stage variables are the initial resource assignment.Furthermore,to adapt itself to different disruption scenarios,the model considers some adjustable variables,such as the decision to cancel the flight in case of disruption,as wait-and-see or second-stage variables.Considering the complexity of integrated models,and the scenario-based decomposable structure of the TRSO model to solve it with better computational performance,we apply the column and row generation(CRG)method that iteratively considers the disruption scenarios.The numerical results confirm the applicability of the proposed TSRO model in providing the AMRCR problem with an integrated and robust solution with an acceptable level of computational tractability.To evaluate the proposed TSRO model,which solves the AMRCR problem in an integrated and robust manner,five Key Performance Indicators(KPIs)like Number of delayed/canceled flights,Average delay time,and Average profit are taken into account.As key results driven by conducting a case study,we show the proposed TSRO model has substantially improved the solutions at all indicators compared with those of the sequential/non-integrated and nominal/non-robust models.The simulated instances used to assess the performance of the proposed model and CRG method reveal that both CPLEX and the CRG method exhibit comparable and nearly optimal performance for small-scale problems.However,for large-scale instances the proposed TSRO model falls short in terms of computational efficiency.Conversely,the proposed CRG method is capable of significantly reducing computational time and the optimality gap to an acceptable level.展开更多
When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game ...When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.展开更多
In this paper,we investigate analytical numerical iterative strategies for the pursuit-evasion game involving spacecraft with leader–follower information.In the proposed problem,the interplay between two spacecraft g...In this paper,we investigate analytical numerical iterative strategies for the pursuit-evasion game involving spacecraft with leader–follower information.In the proposed problem,the interplay between two spacecraft gives rise to a dynamic and real-time game,complicated further by the presence of perturbation.The primary challenge lies in crafting control strategies that are both efficient and applicable to real-time game problems within a nonlinear system.To overcome this challenge,we introduce the model prediction and iterative correction technique proposed in model predictive static programming,enabling the generation of strategies in analytical iterative form for nonlinear systems.Subsequently,we proceed by integrating this model predictive framework into a simplified Stackelberg equilibrium formulation,tailored to address the practical complexities of leader–follower pursuit-evasion scenarios.Simulation results validate the effectiveness and exceptional efficiency of the proposed solution within a receding horizon framework.展开更多
文摘In recent years,the new-style tea-drinking market has expanded rapidly.With the upgrading of consumer demands and the younger generation becoming the primary consumer group,the space experience centered around the“third space”has become a crucial strategy for brands to differentiate themselves.This research focuses on the impact mechanism of spatial scenario design on the brand value of tea-drinking brands,aiming to explore the internal relationships among the key elements of spatial design,brand perception,consumers’emotional connection,and consumption willingness,providing theoretical support and practical references for scenario-based design in the industry.Through a combination of literature research and case-analysis methods,this study systematically reviews relevant domestic and international research on scenario-based design and brand value over the past five years.It selects representative brands as cases,deeply analyzes their spatial design strategies,user feedback,and market performance,and summarizes both successful experiences and existing problems.Scenario-based design is an important means to enhance the brand value of tea-drinking brands,but it needs to follow the four-in-one design principle of“brand consistency,functional diversity,experience coherence,and cost controllability.”In the future,brands should focus on the in-depth exploration and innovative expression of cultural elements,strengthen the multi-functional attributes of spaces,and achieve seamless integration of online and offline scenarios through digital means.In addition,it is recommended to adopt modular design to reduce scenario-updating costs and increase the return on investment.This research provides a theoretical basis and practical path for the optimization of the spatial design of tea-drinking brands,and has important reference value for promoting the high-quality development of the industry.
文摘Objective:To evaluate the efficacy of scenario-based participatory teaching methods in thoracic surgery nursing education.Methods:Sixty undergraduate nursing students were randomly assigned to two groups:a traditional teaching group and a scenario-based participatory teaching group,with 30 students each.The teaching outcomes of both groups were assessed.Results:The clinical reasoning assessment scores of the scenario-based participatory teaching group were significantly higher than those of the traditional group(P<0.05).Additionally,the scenario group demonstrated higher satisfaction levels,superior theoretical and practical skills,improved patient education effectiveness during admission and discharge,and enhanced emergency response coordination(P<0.05).Conclusion:Scenario-based participatory teaching effectively enhances the comprehensive competencies of nursing students in thoracic surgery,demonstrating favorable educational outcomes.
文摘In a first for the African continent,Senegal will host the Dakar 2026 Youth Olympic Games(YOG)from 31 October to 13 November.The Dakar 2026 YOG carry a strong symbolic ambition,embodied by their motto“Africa welcomes,Dakar celebrates.”Host Senegal sees the event as a catalyst for its influence,the modernisation of its infrastructure,and the mobilisation of its youth.
基金supported by a grant from the Korea Creative Content Agency,funded by the Ministry of Culture,Sports and Tourism of the Republic of Korea in 2025,for the project,“Development of AI-based large-scale automatic game verification technology to improve game production verification efficiency for small and medium-sized game companies”(RS 2024-00393500).
文摘GameQualityAssurance(QA)currently relies heavily onmanual testing,a process that is both costly and time-consuming.Traditional script-and log-based automation tools are limited in their ability to detect unpredictable visual bugs,especially those that are context-dependent or graphical in nature.As a result,many issues go unnoticed during manual QA,which reduces overall game quality,degrades the user experience,and creates inefficiencies throughout the development cycle.This study proposes two approaches to address these challenges.The first leverages a Large Language Model(LLM)to directly analyze gameplay videos,detect visual bugs,and automatically generate QA reports in natural language.The second approach introduces a pipeline method:first generating textual descriptions of visual bugs in game videos using the ClipCap model,then using those descriptions as input for the LLM to synthesize QA reports.Through these two multi-faceted approaches,this study evaluates the feasibility of automated game QA systems.To implement this system,we constructed a visual bug database derived from real-world game cases and fine-tuned the ClipCap model for the game video domain.Our proposed approach aims to enhance both efficiency and quality in game development by reducing the burden of manual QA while improving the accuracy of visual bug detection and ensuring consistent,reliable report generation.
基金supported by the National Key R&D Pro-gram of China:Gravitational Wave Detection Project(Nos.2021YFC2026,2021YFC2202601,2021YFC2202603)the National Natural Science Foundation of China(Nos.12172288 and 12472046)。
文摘The problem of maneuvering for a servicing spacecraft(inspector)to inspect a noncooperative spacecraft(evader)in cislunar space is investigated in this paper.The evader,which may be a malfunctioning or uncontrolled satellite,introduces uncertainties due to its potential maneuvering capabilities.To address this challenge,the scenario is modeled as a special orbital game,incorporating the unique complexities of the cislunar environment.A variable-duration,turn-based inspection and anti-inspection game model is designed.The model defines both players'rules,constraints,and victory conditions,providing a framework for non-cooperative inspection.Strategies for both players are developed and validated based on their dynamical properties.The inspector's strategy integrates two-body Lambert transfers with shooting methods,while the evader's strategy aims to maximize the inspector's fuel consumption.Simulation results show that the evader's optimal strategy involves deliberate fluctuations in its lunar periapsis altitude,with the inspector's requiredΔV up to eight times greater than the evader's.The impact of game constraints is evaluated,and the effectiveness of deploying the inspector in low lunar orbit is compared with the inspector at the Earth-Moon Lagrange point L1.The strengths and weaknesses of both are shown.These findings provide valuable insights for future orbital servicing and orbital games.
基金Supported by the National Program on Key Basic Research Project(2020YFA0713600)the National Natural Science Foundation of China(62272214)。
文摘In the era of the Internet of Things,distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices.However,the imbalance between task execution delay and node energy consumption,and the scheduling and adaptation challenges brought about by device heterogeneity,urgently need to be addressed.To tackle this problem,this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance,execution delay,system energy consumption,and node interests.The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction.A real-time task scheduling algorithm based on bilateral matching game is proposed.By designing a bidirectional preference mechanism between tasks and computing nodes,combined with a multi-round stable matching strategy,accurate matching between tasks and nodes is achieved.Simulation results show that compared with the baseline scheme,the proposed algorithm significantly reduces the total execution cost,effectively balances the task execution delay and the energy consumption of compute nodes,and takes into account the interests of each network compute node.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12571549,12571592,12471463,12022113,12101573)。
文摘Vaccination is a key strategy to curb the spread of epidemics.Heterologous vaccination,unlike homologous vaccination which acts on a single target and forms a single immune barrier,covers multiple targets for broader protection.Yet,heterologous vaccination involves a complex decision process that conventional game-theoretic approaches,such as classical,evolutionary,and minority games cannot adequately capture.The parallel minority game(PMG)can handle bounded-rational,multi-choice decisions,but its application in vaccine research remains rare.In this study,we propose a vaccination-transmission coupled dynamic mechanism based on the parallel minority game and simulate it on a two-dimensional lattice.Using actual observational data and a mean-field mathematical model,we verify the effectiveness of this mechanism in simulating realistic vaccination behavior and transmission dynamics.We further analyze the impact of key parameters,such as vaccine efficacy differences and the proportion of individuals eligible for vaccine switching,on containment effectiveness.Our results demonstrate that heterologous vaccination surpasses homologous vaccination in containment effectiveness,particularly when vaccine efficacy varies significantly.This work provides a novel framework and empirical evidence for understanding individual decision-making and population-wide immunity formation in multi-vaccine settings.
基金supported in part by the National Natural Science Foundation of China(62373022,U2241217,62141604)Beijing Natural Science Foundation(4252043,JQ23019)+4 种基金the Fundamental Research Funds for the Central Universities(JKF-2025037448805,JKF-2025086098295)the Aeronautical Science Fund(2023Z034051001)the Academic Excellence Foundation of BUAA for Ph.D. Studentsthe Science and Technology Innovation2030—Key Project of New Generation Artificial Intelligence(2020AAA0108200)the National Key Research and Development Program of China(2022YFB3305600)。
文摘An attack-resilient distributed Nash equilibrium(NE) seeking problem is addressed for noncooperative games of networked systems under malicious cyber-attacks,i.e.,false data injection(FDI) attacks.Different from many existing distributed NE seeking works,it is practical and challenging to get resilient adaptively distributed NE seeking under unknown and unbounded FDI attacks.An attack-resilient NE seeking algorithm that is distributed(i.e.,independent of global information on the graph's algebraic connectivity,Lipschitz and monotone constants of pseudo-gradients,or number of players),is presented by means of incorporating the consensus-based gradient play with a distributed attack identifier so as to achieve simultaneous NE seeking and attack identification asymptotically.Another key characteristic is that FDI attacks are allowed to be unknown and unbounded.By exploiting nonsmooth analysis and stability theory,the global asymptotic convergence of the developed algorithm to the NE is ensured.Moreover,we extend this design to further consider the attack-resilient NE seeking of double-integrator players.Lastly,numerical simulation and practical experiment results are presented to validate the developed algorithms' effectiveness.
基金supported by Science and Technology Project of SGCC(Research on Distributed Cooperative Control of Virtual Power Plants Based on Hybrid Game)(5700-202418337A-2-1-ZX).
文摘This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants(VPP).A generalized credibility-based fuzzy chance constraint programming approach is adopted to address uncertainties stemming from renewable generation and load demand within individual VPPs,while robust optimization techniques manage electricity and thermal price volatilities.Building upon this foundation,a hierarchical Nash-Stackelberg game model is established across multiple VPPs.Within each VPP,a Stackelberg game resolves the strategic interaction between the operator and photovoltaic prosumers(PVP).Among VPPs,a cooperative Nash bargaining model coordinates alliance formation.The problem is decomposed into two subproblems:maximizing coalitional benefits,and allocating cooperative surpluses via payment bargaining,solved distributively using the alternating direction method of multipliers(ADMM).Case studies demonstrate that the proposed strategy significantly enhances the economic efficiency and uncertainty resilience of multi-VPP alliances.
基金The Natural Science Foundation of Heilongjiang Province(No.LH2023E011)Open Fund of National Key Laboratory of Green and Long-Life Road Engineering in Extreme Environment in Changsha University of Science and Technology(No.kfj230105).
文摘To uncover the decision-making mechanisms and evolutionary dynamics of multiple stakeholders in highway noise pollution control,a three-party evolutionary game model involving the government,operators,and the public is constructed.The operation period is divided into different stages for differentiated analysis.A simulation analysis was performed on the Lituo sinking section of the Beijing-Hong Kong-Macao Highway to assess the impact of variations in critical elements on the system.The results indicate that the Lituo sinking section of the Beijing-Hong Kong-Macao Highway is currently in its early stage of development,with the corresponding strategies being active regulation,excessive emissions,and supervision.When the cost of the government’s active regulation decreases from 1×10^(5) to 5×10^(4) yuan,the system converges more rapidly toward the active regulation strategy.When the cost of the operator’s excessive emissions increases from 14.08×10^(6) to 20.00×10^(6) yuan,the system drives the operator toward the standardized emission strategy.In addition,when the cost of public supervision decreases from 15×10^(4) to 5×10^(4) yuan and the compensation paid by operators to the public increases from 1.288×10^(6) to 2.576×10^(6) yuan,the system converges more quickly toward the supervision strategy.The cost of the operator’s excessive emissions serves as the core decision variable for achieving the ideal equilibrium in the three-party game involving government active regulation,operator standardized emissions,and public supervision.
基金supported by the National Natural Science Foundation of China(61702528,61806212,62173336)。
文摘In strategic decision-making tasks,determining how to assign limited costly resource towards the defender and the attacker is a central problem.However,it is hard for pre-allocated resource assignment to adapt to dynamic fighting scenarios,and exists situations where the scenario and rule of the Colonel Blotto(CB)game are too restrictive in real world.To address these issues,a support stage is added as supplementary for pre-allocated results,in which a novel two-stage competitive resource assignment problem is formulated based on CB game and stochastic Lanchester equation(SLE).Further,the force attrition in these two stages is formulated as a stochastic progress to consider the complex fighting progress,including the case that the player with fewer resources defeats the player with more resources and wins the battlefield.For solving this two-stage resource assignment problem,nested solving and no-regret learning are proposed to search the optimal resource assignment strategies.Numerical experiments are taken to analyze the effectiveness of the proposed model and study the assignment strategies in various cases.
基金supported by the National Key Research and Development Program of China(2023YFA1009200)the National Natural Science Foundation of China(12401583,12571482,12521001)+2 种基金the Taishan Scholars Climbing Program of Shandong(TSPD20210302)the Basic Research Program of Jiangsu(BK20240416)the General Program of Philosophy and Social Science Research(PSSR)of Shandong Higher Education Institutions(2024ZSMS007)。
文摘This paper studies an indefinite mean-field game with Markov jump parameters,where all agents'diffusion terms depend on control variables and both state and control average terms(x.^((N)),u.^((N)))are considered.One notable aspect is the relaxation of the assumption regarding the positivity or non-negativity of weight matrices within costs,allowing for zero or even negative values.By virtue of mean-field methods and decomposition techniques,we have derived decentralized strategies presented by Hamiltonian systems and a new type of consistency condition system.These systems consist of fully coupled regime-switching forward-backward stochastic differential equations that do not conform to the Monotonicity condition.The well-posedness of these strategies is established by employing a relaxed compensator method with an easily verifiable Condition(RC)and the decomposition technique.Furthermore,we demonstrate that the resulting decentralized strategies achieve anϵ-Nash equilibrium in the indefinite case without any assumptions on admissible control sets using novel estimates of the disturbed state and cost function.Finally,our theoretical results are applied to resolve a class of mean-variance portfolio selection problems.We provide corresponding numerical simulation results and economic explanations.
基金supported by the National Natural Science Foundation of China(62433014,62373287,62573324,62333005,62273255)in part by the International Exchange Program for Graduate Students of Tongji University(4360143306)+3 种基金in part by the Fundamental Research Funds for Central Universities(22120230311)supported by DeutscheForschungsgemeinschaft(DFG,German Research Foundation)under Germany’s Excellence Strategy(EXC 2075390740016,468094890)support by the Stuttgart Center for Simulation Science(SimTech)the International Max Planck Research School for Intelligent Systems(IMPRS-IS)for supporting Y.Xie。
文摘Dear Editor,This letter proposes a reinforcement learning-based predictive learning algorithm for unknown continuous-time nonlinear systems with observation loss.Firstly,we construct a temporal nonzero-sum game over predictive control input sequences,deriving multiple optimal predictive control input sequences from its solution.
基金supported by the National Natural Science Foundation of China(7157118571201168)
文摘The decisions concerning portfolio selection for army engineering and manufacturing development projects determine the benefit of those projects to the country concerned.Projects are typically selected based on ex ante estimates of future return values,which are usually difficult to specify or only generated after project launch.A scenario-based approach is presented here to address the problem of selecting a project portfolio under incomplete scenario information and interdependency constraints.In the first stage,the relevant dominance concepts of scenario analysis are studied to handle the incomplete information.Then,a scenario-based programming approach is proposed to handle the interdependencies to obtain the projects,whose return values are multi-criteria with interval data.Finally,an illustrative example of army engineering and manufacturing development shows the feasibility and advantages of the scenario-based multi-objective programming approach.
基金partially supported by the Japam Society for the Promotion of Science (JSPS) KAKENHI (Nos. 25420232 and 16K06203)
文摘In software engineering, a scenario describes an anticipated usage of a software system. As scenarios are useful to understand the requirements and functionalities of a software system, the scenario-based analysis is widely used in various tasks, especially in the design stage of software architectures. Although researchers have proposed various scenario-based approaches to analyse software architecture, there are still limitations in this research field, and a key limitation is that scenarios are typically not formally defined and thus may contain ambiguities. As these ambiguities may lead to defects, it is desirable to reduce them as many as possible. In order to reduce ambiguity in scenario-based software architecture analysis, this paper introduces a creative computing approach to scenario-based software requirements analysis. Our work expands this idea in three directions. Firstly, we extend an architecture description language(ADL)-based language – Breeze/ADL to model the software architecture. Secondly, we use a creative rule – combinational rule(CR) to combine the vector clock algorithm for reducing the ambiguities in modelling scenarios. Then, another creative rule – transformational rule(TR) is employed to help to transform our Breeze/ADL model to a popular model – unified modelling language(UML) model. We implement our approach as a plugin of Breeze, and illustrate a running example of modelling a poetry to music system in our case study.Our results show the proposed creative approach is able to reduce ambiguities of the software architecture in practice.
文摘Lots of pervasive computing researchers are working on how to realize the user-centered intelligent pervasive computing environment as Mark Weiser figured out.Task abstraction is the fundamentation of configuration for pervasive application.Based on task-oriented and descriptive properties of scenario,a scenario-based participatory design model was proposed to realize the task abstraction.The design model provided users and domain experts a useful mechanism to build the customized applications by separating system model into domain model and design model.In this design model,domain experts,together with users,stakeholders focus on the logic rules(domain model)and programmers work on the implementation(design model).In order to formalize the model description,a human-agent interaction language to transform users' goals and domain rules into executable scenarios was also discussed.An agent platform-describer used to link design and implementation of scenarios was developed to realize the configuration of applications according to different requirements.The demand bus application showed the design process and the usability of this model.
文摘Motivated by a critical issue of airline planning process,this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruptions.Following the route network scheme and generated flight timetables,aircraft maintenance routing and crew scheduling are critical factors in airline planning and operations cost management.This study considers the simultaneous assignment of aircraft fleet and crew to the scheduled flight while satisfying a set of operational constraints,rules,and regulations.Considering multiple locations for airline maintenance and crew bases,we solve the problem of integrated Aircraft Maintenance Routing and Crew Rostering(AMRCR)to achieve the minimum airline cost.One real challenge to the efficiency of the planning results is the possible disruptions in the initial scheduled flights.Due to the fact that disruption scenarios are expressed discretely with a specified probability,and we provide adjustable decisions under disruption to deal with this disruption risk,we provide a Two-Stage Scenario-Based Robust Optimization(TSRO)model.In this model,here-and-now or first-stage variables are the initial resource assignment.Furthermore,to adapt itself to different disruption scenarios,the model considers some adjustable variables,such as the decision to cancel the flight in case of disruption,as wait-and-see or second-stage variables.Considering the complexity of integrated models,and the scenario-based decomposable structure of the TRSO model to solve it with better computational performance,we apply the column and row generation(CRG)method that iteratively considers the disruption scenarios.The numerical results confirm the applicability of the proposed TSRO model in providing the AMRCR problem with an integrated and robust solution with an acceptable level of computational tractability.To evaluate the proposed TSRO model,which solves the AMRCR problem in an integrated and robust manner,five Key Performance Indicators(KPIs)like Number of delayed/canceled flights,Average delay time,and Average profit are taken into account.As key results driven by conducting a case study,we show the proposed TSRO model has substantially improved the solutions at all indicators compared with those of the sequential/non-integrated and nominal/non-robust models.The simulated instances used to assess the performance of the proposed model and CRG method reveal that both CPLEX and the CRG method exhibit comparable and nearly optimal performance for small-scale problems.However,for large-scale instances the proposed TSRO model falls short in terms of computational efficiency.Conversely,the proposed CRG method is capable of significantly reducing computational time and the optimality gap to an acceptable level.
基金National Natural Science Foundation of China(NSFC61773142,NSFC62303136)。
文摘When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.
基金supported,in part,by the National Natural Science Foundation of China(Nos.12372050 and 62088101)the Zhejiang Provincial Natural Science Foundation of China(No.LR20F030003).
文摘In this paper,we investigate analytical numerical iterative strategies for the pursuit-evasion game involving spacecraft with leader–follower information.In the proposed problem,the interplay between two spacecraft gives rise to a dynamic and real-time game,complicated further by the presence of perturbation.The primary challenge lies in crafting control strategies that are both efficient and applicable to real-time game problems within a nonlinear system.To overcome this challenge,we introduce the model prediction and iterative correction technique proposed in model predictive static programming,enabling the generation of strategies in analytical iterative form for nonlinear systems.Subsequently,we proceed by integrating this model predictive framework into a simplified Stackelberg equilibrium formulation,tailored to address the practical complexities of leader–follower pursuit-evasion scenarios.Simulation results validate the effectiveness and exceptional efficiency of the proposed solution within a receding horizon framework.
