云计算中的群体智能与不确定性知识表示(英文)

The lasting evolution of computing environment, software engineering and interaction methods leads to cloud computing. Cloud computing changes the configuration mode of resources on the Internet and all kinds of resources are virtualized and provided as services. Mass participation and online interaction with social annotations become usual in human daily life. People who own similar interests on the Internet may cluster naturally into scalable and boundless communities and collective intelligence will emerge. Human is taken as an intelligent computing factor, and uncertainty becomes a basic property in cloud computing. Virtualization, soft computing and granular computing will become essential features of cloud computing. Compared with the engineering technological problems of IaaS (Infrastructure as a service), PaaS (Platform as a Service) and SaaS (Software as a Service), collective intelligence and uncertain knowledge representation will be more important frontiers in cloud computing for researchers within the community of intelligence science.

Four development stages of collective intelligence

The new generation of artificial intelligence(AI)research initiated by Chinese scholars conforms to the needs of a new information environment changes,and strives to advance traditional artificial intelligence(AI 1.0)to a new stage of AI 2.0.As one of the important components of AI,collective intelligence(CI 1.0),i.e.,swarm intelligence,is developing to the stage of CI 2.0(crowd intelligence).Through in-depth analysis and informative argumentation,it is found that an incompatibility exists between CI 1.0 and CI 2.0.Therefore,CI 1.5 is introduced to build a bridge between the above two stages,which is based on biocollaborative behavioral mimicry.CI 1.5 is the transition from CI 1.0 to CI 2.0,which contributes to the compatibility of the two stages.Then,a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is given.The meta-synthesis of wisdom,as an improvement of crowd intelligence,is an advanced stage of bionic intelligence,i.e.,CI 3.0.It is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0,and some elaboration is made.As a result,we propose four development stages(CI 1.0,CI 1.5,CI 2.0,and CI 3.0),which form a complete framework for the development of CI.These different stages are progressively improved and have good compatibility.Due to the dominant role of cooperation in the development stages of CI,three types of cooperation in CI are discussed:indirect regulatory cooperation in lower organisms,direct communicative cooperation in higher organisms,and shared intention based collaboration in humans.Labor division is the main form of achieving cooperation and,for this reason,this paper investigates the relationship between the complexity of behavior and types of labor division.Finally,based on the overall understanding of the four development stages of CI,the future development direction and research issues of CI are explored.

Collective Movement Simulation:Methods and Applications

Collective movement simulations are challenging and important in many areas,including life science,mathematics,physics,information science and public safety.In this survey,we provide a comprehensive review of the state-of-the-art techniques for collective movement simulations.We start with a discussion on certain concepts to help beginners understand it more systematically.Then,we analyze the intelligence among different collective objects and the emphasis in different fields.Next,we classify existing collective movement simulation methods into four categories according to their effects,namely versatility,accuracy,dynamic adaptability,and assessment feedback capability.Furthermore,we introduce five applications of layout optimization,emergency control,dispatching,unmanned systems,and other derivative applications.Finally,we summarize possible future research directions.

Emergence mechanisms of group consensus in social networks

Reaching consensus within larger social network groups has emerged as a pivotal concern in the digital age of connectivity.This article redefines group consensus as the emergence of collective intelligence resulting from self-organizing actions and interactions of individuals within a social network group.In our exploration of extant research on group consensus,we illuminate two frequently underestimated,yet noteworthy facets:Dynamism and emergence.In contrast to the conventional perspective of consensus as a mere outcome,we perceive it as an ongoing,dynamic process.This process encompasses self-organized communication and interaction among group members,collectively guiding the group towards cognitive convergence and viewpoint integration.Consequently,it is imperative to redirect our focus from the outcomes of group interactions to an examination of the relationships and processes underpinning consensus formation,thus elucidating the mechanisms responsible for the generation of group consensus.The amalgamation of cognitive contexts and accurate simplification of real-world scenarios for simulation and experimental analysis offers a pragmatic operational approach.This study contributes novel theoretical underpinnings and quantitative insights for establishing and sustaining group consensus within the realm of engineering management practices.Concurrently,it holds substantial importance for advancing the broader research landscape pertaining to social consensus.

Optimal PV array reconfiguration under partial shading condition through dynamic leader based collective intelligence

This paper applies the innovative idea of DLCI to PV array reconfiguration under various PSCs to capture the maxi-mum output power of a PV generation system.DLCI is a hybrid algorithm that integrates multiple meta-heuristic algo-rithms.Through the competition and cooperation of the search mechanisms of different metaheuristic algorithms,the local exploration and global development of the algorithm can be effectively improved to avoid power mismatch of the PV system caused by the algorithm falling into a local optimum.A series of discrete operations are performed on DLCI to solve the discrete optimization problem of PV array reconfiguration.Two structures(DLCI-I and DLCI-II)are designed to verify the effect of increasing the number of sub-optimizers on the optimized performance of DLCI by simulation based on 10 cases of PSCs.The simulation shows that the increase of the number of sub-optimizers only gives a relatively small improvement on the DLCI optimization performance.DLCI has a significant effect on the reduction in the number of power peaks caused by PSC.The PV array-based reconstruction system of DLCI-II is reduced by 4.05%,1.88%,1.68%,0.99%and 3.39%,when compared to the secondary optimization algorithms.

Target detection for multi-UAVs via digital pheromones and navigation algorithm in unknown environments

Coordinating multiple unmanned aerial vehicles(multi-UAVs)is a challenging technique in highly dynamic and sophisticated environments.Based on digital pheromones as well as current mainstream unmanned system controlling algorithms,we propose a strategy for multi-UAVs to acquire targets with limited prior knowledge.In particular,we put forward a more reasonable and effective pheromone update mechanism,by improving digital pheromone fusion algorithms for different semantic pheromones and planning individuals’probabilistic behavioral decision-making schemes.Also,inspired by the flocking model in nature,considering the limitations of some individuals in perception and communication,we design a navigation algorithm model on top of Olfati-Saber’s algorithm for flocking control,by further replacing the pheromone scalar to a vector.Simulation results show that the proposed algorithm can yield superior performance in terms of coverage,detection and revisit efficiency,and the capability of obstacle avoidance.

Hybrid Predictive Ensembles:Synergies Between Human and Computational Forecasts

An increasing proportion of decisions,design choices,and predictions are being made by hybrid groups consisting of humans and artificial intelligence(AI).In this paper,we provide analytic foundations that explain the potential benefits of hybrid groups on predictive tasks,the primary use of AI.Our analysis relies on interpretive and generative signal frameworks as well as a distinction between the big data used by AI and the thick,often narrative data used by humans.We derive several conditions on accuracy and correlation necessary for humans to remain in the loop.We conclude that human adaptability along with the potential for atypical cases that mislead AI will likely mean that humans always add value on predictive tasks.

Connotation and Determinants of Web Collective Intelligence

With continuous development of network technology, users in network community are promoted to interact deeply, and remarkable web collective intelligence emerges in the process. As a relatively new concept, the connotation of web collective intelligence is preliminarily explored in this paper, where the network community is taken as the environment, expert users as the subject, and web comments as the carrier. Meanwhile, taking Wikipedia as an example, by means of questionnaire survey and structural equation model, a more systematic index system is constructed from the perspective of user characteristics to explore determinants of web collective intelligence quality, and potential influence of user attributes on user behavior.