People have been engaged in sports activities both individually and collectively for years.Sports consumption,which refers to the process that covers many issues related to sports in the form of playing,watching,liste...People have been engaged in sports activities both individually and collectively for years.Sports consumption,which refers to the process that covers many issues related to sports in the form of playing,watching,listening or reading,is a form of human behavior.The satisfaction of the four marketing components of product,price,distribution and promotion by using the leisure time of the sports consumer effectively and ensuring its continuity in the future process can be ensured by effective utilization of facilities and quality recreation activities.Consumer behaviors,which have a very complex structure,are seen in the form of choosing,buying,using and obtaining.With this study,it is aimed to determine the mediating role of consumer decision-making styles in determining the effect of marketing components in the consumption of sports activities on the satisfaction of sports consumers.In this direction,data were collected in the province of Istanbul,which was determined as the sample.Data were obtained with a questionnaire form created on Google Form.These data were analyzed in line with the model and hypotheses created with these data and it was determined that the marketing components of sports consumption have an impact on the sports consumer and it was concluded that consumer decision-making styles have a positive mediating effect in this regard.展开更多
This study focuses on the construction and application of intelligent financial decision-making models driven by generative artificial intelligence(AI).It analyzes the mechanisms by which generative AI empowers financ...This study focuses on the construction and application of intelligent financial decision-making models driven by generative artificial intelligence(AI).It analyzes the mechanisms by which generative AI empowers financial decision-making within a dual framework of dynamic knowledge evolution and risk control.The research reveals that generative AI,with its superior data processing,pattern recognition,and autonomous learning capabilities,can transcend the limitations of traditional decision-making models,facilitating a significant shift from causal inference to probabilistic creation in decision-making paradigms.By systematically constructing an intelligent financial decision-making model that includes data governance,core engine,and decision output layers,the study clarifies the functional roles and collaborative mechanisms of each layer.Additionally,it addresses key challenges in technology application,institutional adaptation,and organizational transformation by proposing systematic strategies for technical risk management,institutional innovation,and organizational capability enhancement,aiming to provide robust theoretical support and practical guidance for the intelligent transformation of corporate financial decision-making.展开更多
To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on ...To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.展开更多
The high maneuverability of modern fighters in close air combat imposes significant cognitive demands on pilots,making rapid,accurate decision-making challenging.While reinforcement learning(RL)has shown promise in th...The high maneuverability of modern fighters in close air combat imposes significant cognitive demands on pilots,making rapid,accurate decision-making challenging.While reinforcement learning(RL)has shown promise in this domain,the existing methods often lack strategic depth and generalization in complex,high-dimensional environments.To address these limitations,this paper proposes an optimized self-play method enhanced by advancements in fighter modeling,neural network design,and algorithmic frameworks.This study employs a six-degree-of-freedom(6-DOF)F-16 fighter model based on open-source aerodynamic data,featuring airborne equipment and a realistic visual simulation platform,unlike traditional 3-DOF models.To capture temporal dynamics,Long Short-Term Memory(LSTM)layers are integrated into the neural network,complemented by delayed input stacking.The RL environment incorporates expert strategies,curiositydriven rewards,and curriculum learning to improve adaptability and strategic decision-making.Experimental results demonstrate that the proposed approach achieves a winning rate exceeding90%against classical single-agent methods.Additionally,through enhanced 3D visual platforms,we conducted human-agent confrontation experiments,where the agent attained an average winning rate of over 75%.The agent's maneuver trajectories closely align with human pilot strategies,showcasing its potential in decision-making and pilot training applications.This study highlights the effectiveness of integrating advanced modeling and self-play techniques in developing robust air combat decision-making systems.展开更多
Gastrointestinal and hepatic disorders exhibit significant heterogeneity,charac-terized by complex and diverse clinical phenotypes.Most lesions present without typical symptoms in their early stages,which poses substa...Gastrointestinal and hepatic disorders exhibit significant heterogeneity,charac-terized by complex and diverse clinical phenotypes.Most lesions present without typical symptoms in their early stages,which poses substantial challenges for early clinical identification and intervention.As an interdisciplinary field at the forefront of technology,artificial intelligence(AI)integrates theoretical inno-vation,algorithm development,and engineering applications,triggering para-digm shifts within the medical field.Current research trends indicate that AI technology is progressively permeating the entire diagnostic and therapeutic process for gastrointestinal and hepatic disorders,facilitating intelligent transformations in precise lesion detection,optimization of treatment decisions,and prognosis evaluation through the integration of different modal data,construction of intelligent algorithms,and establishment of clinical verification systems.This article systematically reviews the latest advancements in AI technology concerning the diagnosis and treatment of gastrointestinal diseases(such as inflammatory bowel disease and digestive system tumors)and hepatic diseases(including hepato-cirrhosis and liver cancer),emphasizing its application value and transformative potential in critical areas such as imaging omics analysis,endoscopic intelligent identification,and personalized treatment prediction.展开更多
With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medici...With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medicine and transportation.In this paper,we systematically expound on the intelligent decision-making technology and prospects driven by large AI models.Specifically,we first review the development of large AI models in recent years.Then,from the perspective of methods,we introduce important theories and technologies of large decision models,such as model architecture and model adaptation.Next,from the perspective of applications,we introduce the cutting-edge applications of large decision models in various fields,such as autonomous driving and knowledge decision-making.Finally,we discuss existing challenges,such as security issues,decision bias and hallucination phenomenon as well as future prospects,from both technology development and domain applications.We hope this review paper can help researchers understand the important progress of intelligent decision-making driven by large AI models.展开更多
Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy ...Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy logic,and multi-criteria decision-making(MCDM)in transportation research.A comprehensive literature search was conducted in the Scopus database,utilizing carefully selected AI,fuzzy,and MCDM keywords.Studies were rigorously screened according to explicit inclusion and exclusion criteria,resulting in 73 eligible publications spanning 2006-2025.The review protocol included transparent data extraction on methodological approaches,application domains,and geographic distribution.Key findings highlight the prevalence of hybrid fuzzyAHPand TOPSIS methods,the widespread integration of machine learning for prediction and optimization,and a predominant focus on logistics and infrastructure planning within the transportation sector.Geographic analysis underscores a marked concentration of research activity in Asia,while other regions remain underrepresented,signaling the need for broader international collaboration.The review also addresses persistent challenges such asmethodological complexity,data limitations,and model interpretability.Future research directions are proposed,including the integration of reinforcement learning,real-time analytics,and big data-driven adaptive solutions.This study offers a comprehensive synthesis and critical perspective,serving as a valuable reference for researchers,practitioners,and policymakers seeking to enhance the efficiency,resilience,and sustainability of transportation systems through intelligent decision-making frameworks.展开更多
The investigation by Zhu et al on the assessment of cellular proliferation markers to assist clinical decision-making in patients with hepatocellular carcinoma(HCC)using a machine learning model-based approach is a sc...The investigation by Zhu et al on the assessment of cellular proliferation markers to assist clinical decision-making in patients with hepatocellular carcinoma(HCC)using a machine learning model-based approach is a scientific approach.This study looked into the possibilities of using a Ki-67(a marker for cell proliferation)expression-based machine learning model to help doctors make decisions about treatment options for patients with HCC before surgery.The study used reconstructed tomography images of 164 patients with confirmed HCC from the intratumoral and peritumoral regions.The features were chosen using various statistical methods,including least absolute shrinkage and selection operator regression.Also,a nomogram was made using Radscore and clinical risk factors.It was tested for its ability to predict receiver operating characteristic curves and calibration curves,and its clinical benefits were found using decision curve analysis.The calibration curve demonstrated excellent consistency between predicted and actual probability,and the decision curve confirmed its clinical benefit.The proposed model is helpful for treating patients with HCC because the predicted and actual probabilities are very close to each other,as shown by the decision curve analysis.Further prospective studies are required,incorporating a multicenter and large sample size design,additional relevant exclusion criteria,information on tumors(size,number,and grade),and cancer stage to strengthen the clinical benefit in patients with HCC.展开更多
Objective To evaluate the clinical value of siltuximab in the treatment of Castleman disease based on multicriteria decision analysis(MCDA)and evidence and value:Impact on decision making(EVIDEM)framework.Methods The ...Objective To evaluate the clinical value of siltuximab in the treatment of Castleman disease based on multicriteria decision analysis(MCDA)and evidence and value:Impact on decision making(EVIDEM)framework.Methods The evidence matrix for quantitative analysis of MCDA was extracted through literature research,and the weight of each evaluation index was calculated by the maximum differentiation measure in conjoint analysis.Besides,the clinical value of siltuximab in the treatment of Castleman disease was analyzed quantitatively and qualitatively based on the results of expert questionnaire surveys.Results and Conclusion The clinical value score of siltuximab was 0.491,and the weight ratio of“therapeutic benefit”(15.39%),“drug effectiveness”(14.46%)and“drug safety”(11.43%)were the three largest.Among the indexes of“drug effectiveness”“drug safety”“patient reported outcome”“therapeutic benefit”and“non-medical cost”,siltuximab for Castleman disease was considered to be a more valuable treatment option than other first-line therapies.By qualitative analysis,57%experts believed that siltuximab was a better treatment option.The indexes that contribute the most to the overall clinical value of siltuximab are“therapeutic benefit”“drug effectiveness”and“quality of evidence”,while the indexes that have a negative impact on the clinical value of siltuximab is“drug treatment cost”.展开更多
Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique f...Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.展开更多
As multi-discipline coupling and components interference often affect the aircraft configuration decision-making and analysis during conceptual design process, this article presents an approach of multidimensional gam...As multi-discipline coupling and components interference often affect the aircraft configuration decision-making and analysis during conceptual design process, this article presents an approach of multidimensional game theory based on aircraft compo- nents to deal with this problem. The idea is that the configuration decision-making process is regarded as the game for different disciplines and technologies, and the aircraft components are players. The payoff function with highest total gain means that ac- cording to the game protocols and multidimensional theory, the optimal aircraft configuration within the strategy set will be cho- sen. The decision-making model is applied to conceptual design process of the high altitude long endurance （HALE） unmanned aerial vehicle （UAV） based on the assessment of technological risk. The obtained optimum configuration is quite consistent with the current HALE UAV development trends. Thus, taking into account the coupling and interference factors, the multidimensional gaming model based on aircraft components will be an effective analysis method in the decision-making process of aircraft optimum configuration.展开更多
The optimization of black-start decision-making plays an important role in the rapid restoration of a power system after a major failure/outage.With the introduction of the concept of smart grids and the development o...The optimization of black-start decision-making plays an important role in the rapid restoration of a power system after a major failure/outage.With the introduction of the concept of smart grids and the development of real-time communication networks,the black-start decision-makers are no longer limited to only one or a few power system experts such as dispatchers,but rather a large group of professional people in practice.The overall behaviors of a large decision-making group of decision-makers/experts are more complicated and unpredictable.However,the existing methods for black-start decision-making cannot handle the situations with a large group of decision-makers.Given this background,a clustering algorithm is presented to optimize the black-start decision-making problem with a large group of decision-makers.Group decision-making preferences are obtained by clustering analysis,and the final black-start decision-making results are achieved by combining the weights of black-start indexes and the preferences of the decision-making group.The effectiveness of the proposed method is validated by a practical case.This work extends the black-start decision-making problem to situations with a large group of decision-makers.展开更多
In this paper, for multi objective decision making, the defects on the commonly used interactive methods based on the satisfactoriness criterion is studied. Then a class of two stage interactive method based on the...In this paper, for multi objective decision making, the defects on the commonly used interactive methods based on the satisfactoriness criterion is studied. Then a class of two stage interactive method based on the satisfactoriness criterion is proposed for improvement with the satisfactoriness criterion being determined through the collection of the decision makers preference information. An application example is presented for illustration of applicability of the method.展开更多
The importance and complexity of prioritizing construction projects (PCP) in urban road network planning lead to the necessity to develop an aided decision making program (ADMP). Cost benefit ratio model and stage rol...The importance and complexity of prioritizing construction projects (PCP) in urban road network planning lead to the necessity to develop an aided decision making program (ADMP). Cost benefit ratio model and stage rolled method are chosen as the theoretical foundations of the program, and then benefit model is improved to accord with the actuality of urban traffic in China. Consequently, program flows, module functions and data structures are designed, and particularly an original data structure of road ...展开更多
The presently existing decision making method for problem of goal type, i.e. the goal programming, is popular to some extent. In this paper we analyzed the features of the problem and the method,based on which we foun...The presently existing decision making method for problem of goal type, i.e. the goal programming, is popular to some extent. In this paper we analyzed the features of the problem and the method,based on which we found some defects of the method and pointed out these defects. To overcome these defects we absorbed the spirit and exploited concepts of evaluation criterion and the fault measure of evaluation criterion. We proposed and applied a method with an evaluation criterion, after which we also p...展开更多
With the inbound tourism market data supported by National Natural Science Foundation of China as the basis, this study through researching the tourism decision-making behaviors of inbound tourists, adopting questionn...With the inbound tourism market data supported by National Natural Science Foundation of China as the basis, this study through researching the tourism decision-making behaviors of inbound tourists, adopting questionnaire survey among European tourists in Xi'an City, analyzed their tourism decision behaviors and influencing factors, and aimed to propose pertinent suggestions for the expansion of European tourist market for Xi'an City.展开更多
The command and control（C2） is a decision-making process based on human cognition,which contains operational,physical,and human characteristics,so it takes on uncertainty and complexity.As a decision support approac...The command and control（C2） is a decision-making process based on human cognition,which contains operational,physical,and human characteristics,so it takes on uncertainty and complexity.As a decision support approach,Bayesian networks（BNs） provide a framework in which a decision is made by combining the experts＇ knowledge and the specific data.In addition,an expert system represented by human cognitive framework is adopted to express the real-time decision-making process of the decision maker.The combination of the Bayesian decision support and human cognitive framework in the C2 of a specific application field is modeled and executed by colored Petri nets（CPNs）,and the consequences of execution manifest such combination can perfectly present the decision-making process in C2.展开更多
A distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm （NGA） is proposed. By using blackboard-based communication paradigm and shared data structure, multiple...A distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm （NGA） is proposed. By using blackboard-based communication paradigm and shared data structure, multiple decision-makers （DMs） can collaboratively solve the tasks-platforms allocation scheduling problems dynamically through the coordinator. This methodo- logy combined with NGA maximizes tasks execution accuracy, also minimizes the weighted total workload of the DM which is measured in terms of intra-DM and inter-DM coordination. The intra-DM employs an optimization-based scheduling algorithm to match the tasks-platforms assignment request with its own platforms. The inter-DM coordinates the exchange of collaborative request information and platforms among DMs using the blackboard architecture. The numerical result shows that the proposed black- board DM framework based on NGA can obtain a near-optimal solution for the tasks-platforms collaborative planning problem. The assignment of platforms-tasks and the patterns of coordination can achieve a nice trade-off between intra-DM and inter-DM coordination workload.展开更多
For group decision-making problems with linguistic assessment information, a new method based on two-tuple and WC-OWA operator is proposed, in which the criteria＇s weights and the decision-makers＇ preference informa...For group decision-making problems with linguistic assessment information, a new method based on two-tuple and WC-OWA operator is proposed, in which the criteria＇s weights and the decision-makers＇ preference information might take the form of linguistic grade, or might be between two continuous linguistic grades, or might be linguistic interval, or might be default. In this method, all linguistic values are transformed into two-tuple, and an aggregative decision-making matrix is obtained by using interval operation. The group aggregative values of each criterion on alternatives are computed by using a WC-OWA operator, the aggregative values on alternatives are worked out, and transformed into two-tuple. And the rank of the alternatives is obtained by using the order property of two-tuple. An example shows the feasibility and effectiveness of the proposed method.展开更多
The modern war features a highly distributed coordination. In the face of great time constrains, it is important to change command organizations to adapt to the real environment. Therefore it's a key step to set u...The modern war features a highly distributed coordination. In the face of great time constrains, it is important to change command organizations to adapt to the real environment. Therefore it's a key step to set up adaptive C2 teams. In this paper, the relational problems about distributed C2 organizational structure adaptation are discussed, and the methodology for team decision making design based on the object oriented technique is studied.展开更多
文摘People have been engaged in sports activities both individually and collectively for years.Sports consumption,which refers to the process that covers many issues related to sports in the form of playing,watching,listening or reading,is a form of human behavior.The satisfaction of the four marketing components of product,price,distribution and promotion by using the leisure time of the sports consumer effectively and ensuring its continuity in the future process can be ensured by effective utilization of facilities and quality recreation activities.Consumer behaviors,which have a very complex structure,are seen in the form of choosing,buying,using and obtaining.With this study,it is aimed to determine the mediating role of consumer decision-making styles in determining the effect of marketing components in the consumption of sports activities on the satisfaction of sports consumers.In this direction,data were collected in the province of Istanbul,which was determined as the sample.Data were obtained with a questionnaire form created on Google Form.These data were analyzed in line with the model and hypotheses created with these data and it was determined that the marketing components of sports consumption have an impact on the sports consumer and it was concluded that consumer decision-making styles have a positive mediating effect in this regard.
文摘This study focuses on the construction and application of intelligent financial decision-making models driven by generative artificial intelligence(AI).It analyzes the mechanisms by which generative AI empowers financial decision-making within a dual framework of dynamic knowledge evolution and risk control.The research reveals that generative AI,with its superior data processing,pattern recognition,and autonomous learning capabilities,can transcend the limitations of traditional decision-making models,facilitating a significant shift from causal inference to probabilistic creation in decision-making paradigms.By systematically constructing an intelligent financial decision-making model that includes data governance,core engine,and decision output layers,the study clarifies the functional roles and collaborative mechanisms of each layer.Additionally,it addresses key challenges in technology application,institutional adaptation,and organizational transformation by proposing systematic strategies for technical risk management,institutional innovation,and organizational capability enhancement,aiming to provide robust theoretical support and practical guidance for the intelligent transformation of corporate financial decision-making.
基金supported by the Major Projects for Science and Technology Innovation 2030(2018AAA0100805).
文摘To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.
基金co-supported by the National Natural Science Foundation of China(No.91852115)。
文摘The high maneuverability of modern fighters in close air combat imposes significant cognitive demands on pilots,making rapid,accurate decision-making challenging.While reinforcement learning(RL)has shown promise in this domain,the existing methods often lack strategic depth and generalization in complex,high-dimensional environments.To address these limitations,this paper proposes an optimized self-play method enhanced by advancements in fighter modeling,neural network design,and algorithmic frameworks.This study employs a six-degree-of-freedom(6-DOF)F-16 fighter model based on open-source aerodynamic data,featuring airborne equipment and a realistic visual simulation platform,unlike traditional 3-DOF models.To capture temporal dynamics,Long Short-Term Memory(LSTM)layers are integrated into the neural network,complemented by delayed input stacking.The RL environment incorporates expert strategies,curiositydriven rewards,and curriculum learning to improve adaptability and strategic decision-making.Experimental results demonstrate that the proposed approach achieves a winning rate exceeding90%against classical single-agent methods.Additionally,through enhanced 3D visual platforms,we conducted human-agent confrontation experiments,where the agent attained an average winning rate of over 75%.The agent's maneuver trajectories closely align with human pilot strategies,showcasing its potential in decision-making and pilot training applications.This study highlights the effectiveness of integrating advanced modeling and self-play techniques in developing robust air combat decision-making systems.
文摘Gastrointestinal and hepatic disorders exhibit significant heterogeneity,charac-terized by complex and diverse clinical phenotypes.Most lesions present without typical symptoms in their early stages,which poses substantial challenges for early clinical identification and intervention.As an interdisciplinary field at the forefront of technology,artificial intelligence(AI)integrates theoretical inno-vation,algorithm development,and engineering applications,triggering para-digm shifts within the medical field.Current research trends indicate that AI technology is progressively permeating the entire diagnostic and therapeutic process for gastrointestinal and hepatic disorders,facilitating intelligent transformations in precise lesion detection,optimization of treatment decisions,and prognosis evaluation through the integration of different modal data,construction of intelligent algorithms,and establishment of clinical verification systems.This article systematically reviews the latest advancements in AI technology concerning the diagnosis and treatment of gastrointestinal diseases(such as inflammatory bowel disease and digestive system tumors)and hepatic diseases(including hepato-cirrhosis and liver cancer),emphasizing its application value and transformative potential in critical areas such as imaging omics analysis,endoscopic intelligent identification,and personalized treatment prediction.
基金supported by the National Natural Science Foundation of China(Grant 62293545)Shenzhen Science and Technology Program(Grant ZDSYS20220323112000001).
文摘With the rapid development of large AI models,large decision models have further broken through the limits of human cognition and promoted the innovation of decision-making paradigms in extensive fields such as medicine and transportation.In this paper,we systematically expound on the intelligent decision-making technology and prospects driven by large AI models.Specifically,we first review the development of large AI models in recent years.Then,from the perspective of methods,we introduce important theories and technologies of large decision models,such as model architecture and model adaptation.Next,from the perspective of applications,we introduce the cutting-edge applications of large decision models in various fields,such as autonomous driving and knowledge decision-making.Finally,we discuss existing challenges,such as security issues,decision bias and hallucination phenomenon as well as future prospects,from both technology development and domain applications.We hope this review paper can help researchers understand the important progress of intelligent decision-making driven by large AI models.
文摘Transportation systems are rapidly transforming in response to urbanization,sustainability challenges,and advances in digital technologies.This review synthesizes the intersection of artificial intelligence(AI),fuzzy logic,and multi-criteria decision-making(MCDM)in transportation research.A comprehensive literature search was conducted in the Scopus database,utilizing carefully selected AI,fuzzy,and MCDM keywords.Studies were rigorously screened according to explicit inclusion and exclusion criteria,resulting in 73 eligible publications spanning 2006-2025.The review protocol included transparent data extraction on methodological approaches,application domains,and geographic distribution.Key findings highlight the prevalence of hybrid fuzzyAHPand TOPSIS methods,the widespread integration of machine learning for prediction and optimization,and a predominant focus on logistics and infrastructure planning within the transportation sector.Geographic analysis underscores a marked concentration of research activity in Asia,while other regions remain underrepresented,signaling the need for broader international collaboration.The review also addresses persistent challenges such asmethodological complexity,data limitations,and model interpretability.Future research directions are proposed,including the integration of reinforcement learning,real-time analytics,and big data-driven adaptive solutions.This study offers a comprehensive synthesis and critical perspective,serving as a valuable reference for researchers,practitioners,and policymakers seeking to enhance the efficiency,resilience,and sustainability of transportation systems through intelligent decision-making frameworks.
文摘The investigation by Zhu et al on the assessment of cellular proliferation markers to assist clinical decision-making in patients with hepatocellular carcinoma(HCC)using a machine learning model-based approach is a scientific approach.This study looked into the possibilities of using a Ki-67(a marker for cell proliferation)expression-based machine learning model to help doctors make decisions about treatment options for patients with HCC before surgery.The study used reconstructed tomography images of 164 patients with confirmed HCC from the intratumoral and peritumoral regions.The features were chosen using various statistical methods,including least absolute shrinkage and selection operator regression.Also,a nomogram was made using Radscore and clinical risk factors.It was tested for its ability to predict receiver operating characteristic curves and calibration curves,and its clinical benefits were found using decision curve analysis.The calibration curve demonstrated excellent consistency between predicted and actual probability,and the decision curve confirmed its clinical benefit.The proposed model is helpful for treating patients with HCC because the predicted and actual probabilities are very close to each other,as shown by the decision curve analysis.Further prospective studies are required,incorporating a multicenter and large sample size design,additional relevant exclusion criteria,information on tumors(size,number,and grade),and cancer stage to strengthen the clinical benefit in patients with HCC.
文摘Objective To evaluate the clinical value of siltuximab in the treatment of Castleman disease based on multicriteria decision analysis(MCDA)and evidence and value:Impact on decision making(EVIDEM)framework.Methods The evidence matrix for quantitative analysis of MCDA was extracted through literature research,and the weight of each evaluation index was calculated by the maximum differentiation measure in conjoint analysis.Besides,the clinical value of siltuximab in the treatment of Castleman disease was analyzed quantitatively and qualitatively based on the results of expert questionnaire surveys.Results and Conclusion The clinical value score of siltuximab was 0.491,and the weight ratio of“therapeutic benefit”(15.39%),“drug effectiveness”(14.46%)and“drug safety”(11.43%)were the three largest.Among the indexes of“drug effectiveness”“drug safety”“patient reported outcome”“therapeutic benefit”and“non-medical cost”,siltuximab for Castleman disease was considered to be a more valuable treatment option than other first-line therapies.By qualitative analysis,57%experts believed that siltuximab was a better treatment option.The indexes that contribute the most to the overall clinical value of siltuximab are“therapeutic benefit”“drug effectiveness”and“quality of evidence”,while the indexes that have a negative impact on the clinical value of siltuximab is“drug treatment cost”.
文摘Enhancing Autonomous Decision-Making (ADM) for unmanned combat aerial vehicle formations in beyond-visual-range air combat is pivotal for future battlefields, whereas the predominant reinforcement learning technique for ADM has been proven to be inadequately fitting complex tactical Unit Coordination (UC), limiting the integrity of decision-making for formations. This study proposes a knowledge-enhanced ADM method, with a focus on UC, to elevate formation combat effectiveness. The main innovation is integrating data mining technique with tactical knowledge mining and integration. Foremost, based on Frequent Event Arrangement Mining (FEAM) theory, a cross-channel UC knowledge mining method is designed by introducing data flow, which is capable of capturing dynamic coordinative action sequences. Then, a dual-mode knowledge integration method is proposed by employing the Graph Attention Network (GAT) and attenuated structural similarity, bolstering the interplay between autonomous UC tactics fitting and knowledge injection. The experimental results demonstrate that the algorithm surpasses the existing methods, providing more strategic maneuver trajectories and a win rate of more than 90% in different scenarios. The method is promising to augment the autonomous operational capabilities of unmanned formations and drive the evolution of combat effectiveness.
文摘As multi-discipline coupling and components interference often affect the aircraft configuration decision-making and analysis during conceptual design process, this article presents an approach of multidimensional game theory based on aircraft compo- nents to deal with this problem. The idea is that the configuration decision-making process is regarded as the game for different disciplines and technologies, and the aircraft components are players. The payoff function with highest total gain means that ac- cording to the game protocols and multidimensional theory, the optimal aircraft configuration within the strategy set will be cho- sen. The decision-making model is applied to conceptual design process of the high altitude long endurance （HALE） unmanned aerial vehicle （UAV） based on the assessment of technological risk. The obtained optimum configuration is quite consistent with the current HALE UAV development trends. Thus, taking into account the coupling and interference factors, the multidimensional gaming model based on aircraft components will be an effective analysis method in the decision-making process of aircraft optimum configuration.
基金supported by National Natural Science Foundation of China (No.51007080)National High Technology Research and Development Program of China (863 Program) (No.2011AA05A105)+1 种基金the Fundamental Research Funds for the Central Universities (No.2012QNA4011)key project from Zhejiang Electric Power Corporation
文摘The optimization of black-start decision-making plays an important role in the rapid restoration of a power system after a major failure/outage.With the introduction of the concept of smart grids and the development of real-time communication networks,the black-start decision-makers are no longer limited to only one or a few power system experts such as dispatchers,but rather a large group of professional people in practice.The overall behaviors of a large decision-making group of decision-makers/experts are more complicated and unpredictable.However,the existing methods for black-start decision-making cannot handle the situations with a large group of decision-makers.Given this background,a clustering algorithm is presented to optimize the black-start decision-making problem with a large group of decision-makers.Group decision-making preferences are obtained by clustering analysis,and the final black-start decision-making results are achieved by combining the weights of black-start indexes and the preferences of the decision-making group.The effectiveness of the proposed method is validated by a practical case.This work extends the black-start decision-making problem to situations with a large group of decision-makers.
文摘In this paper, for multi objective decision making, the defects on the commonly used interactive methods based on the satisfactoriness criterion is studied. Then a class of two stage interactive method based on the satisfactoriness criterion is proposed for improvement with the satisfactoriness criterion being determined through the collection of the decision makers preference information. An application example is presented for illustration of applicability of the method.
文摘The importance and complexity of prioritizing construction projects (PCP) in urban road network planning lead to the necessity to develop an aided decision making program (ADMP). Cost benefit ratio model and stage rolled method are chosen as the theoretical foundations of the program, and then benefit model is improved to accord with the actuality of urban traffic in China. Consequently, program flows, module functions and data structures are designed, and particularly an original data structure of road ...
文摘The presently existing decision making method for problem of goal type, i.e. the goal programming, is popular to some extent. In this paper we analyzed the features of the problem and the method,based on which we found some defects of the method and pointed out these defects. To overcome these defects we absorbed the spirit and exploited concepts of evaluation criterion and the fault measure of evaluation criterion. We proposed and applied a method with an evaluation criterion, after which we also p...
基金Supported by National Natural Science Foundation(49271037)Scientific Research Foundation of Xi’an University of Arts & Science(KYC200732)~~
文摘With the inbound tourism market data supported by National Natural Science Foundation of China as the basis, this study through researching the tourism decision-making behaviors of inbound tourists, adopting questionnaire survey among European tourists in Xi'an City, analyzed their tourism decision behaviors and influencing factors, and aimed to propose pertinent suggestions for the expansion of European tourist market for Xi'an City.
基金supported by the National Natural Science Foundation of China (60874068)
文摘The command and control（C2） is a decision-making process based on human cognition,which contains operational,physical,and human characteristics,so it takes on uncertainty and complexity.As a decision support approach,Bayesian networks（BNs） provide a framework in which a decision is made by combining the experts＇ knowledge and the specific data.In addition,an expert system represented by human cognitive framework is adopted to express the real-time decision-making process of the decision maker.The combination of the Bayesian decision support and human cognitive framework in the C2 of a specific application field is modeled and executed by colored Petri nets（CPNs）,and the consequences of execution manifest such combination can perfectly present the decision-making process in C2.
基金supported by the National Aerospace Science Foundation of China(20138053038)the Graduate Starting Seed Fund of Northwestern Polytechnical University(Z2015111)
文摘A distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm （NGA） is proposed. By using blackboard-based communication paradigm and shared data structure, multiple decision-makers （DMs） can collaboratively solve the tasks-platforms allocation scheduling problems dynamically through the coordinator. This methodo- logy combined with NGA maximizes tasks execution accuracy, also minimizes the weighted total workload of the DM which is measured in terms of intra-DM and inter-DM coordination. The intra-DM employs an optimization-based scheduling algorithm to match the tasks-platforms assignment request with its own platforms. The inter-DM coordinates the exchange of collaborative request information and platforms among DMs using the blackboard architecture. The numerical result shows that the proposed black- board DM framework based on NGA can obtain a near-optimal solution for the tasks-platforms collaborative planning problem. The assignment of platforms-tasks and the patterns of coordination can achieve a nice trade-off between intra-DM and inter-DM coordination workload.
基金the Key Project of National Natural Science Foundation of China (70631004)the National Natural Science Foundation of China (70771115)
文摘For group decision-making problems with linguistic assessment information, a new method based on two-tuple and WC-OWA operator is proposed, in which the criteria＇s weights and the decision-makers＇ preference information might take the form of linguistic grade, or might be between two continuous linguistic grades, or might be linguistic interval, or might be default. In this method, all linguistic values are transformed into two-tuple, and an aggregative decision-making matrix is obtained by using interval operation. The group aggregative values of each criterion on alternatives are computed by using a WC-OWA operator, the aggregative values on alternatives are worked out, and transformed into two-tuple. And the rank of the alternatives is obtained by using the order property of two-tuple. An example shows the feasibility and effectiveness of the proposed method.
文摘The modern war features a highly distributed coordination. In the face of great time constrains, it is important to change command organizations to adapt to the real environment. Therefore it's a key step to set up adaptive C2 teams. In this paper, the relational problems about distributed C2 organizational structure adaptation are discussed, and the methodology for team decision making design based on the object oriented technique is studied.
