Feature evaluation and selection with cooperative game theory

Recent years, various information theoretic based measurements have been proposed to remove redundant features from high-dimensional data set as many as possible. However, most traditional Information-theoretic based selectors will ignore some features which have strong discriminatory power as a group but are weak as individuals. To cope with this problem, this paper introduces a cooperative game theory based framework to evaluate the power of each feature. The power can be served as a metric of the importance of each feature according to the intricate and intrinsic interrelation among features. Then a general filter feature selection scheme is presented based on the introduced framework to handle the feature selection problem. To verify the effectiveness of our method, experimental comparisons with several other existing feature selection methods on fifteen UCI data sets are carried out using four typical classifiers. The results show that the proposed algorithm achieves better results than other methods in most cases.     

Distributed cooperative control algorithm for optimal power sharing for AC microgrids using Cournot game theory

Neural Computing and Applications - Research on the optimal power allocation of large-scale distributed generator (DG) units based on user power generation to access microgrids (MGs) in a...     

一种考虑DMU间交叉竞争的博弈效率DEA评价方法

针对绩效评价过程中一般只考虑DMU与评价者之间的合作竞争而忽视DMU间的非合作竞争的博弈,引入交叉竞争的博弈理念,将评价问题界定为评价者与DMU间合作竞争与博弈、DMU间交叉竞争的博弈两大类;考虑到在交叉竞争的博弈情境下,DMU的指标值不再是固定不变,而是随之动态调整的特点,设计交叉竞争的博弈规则,并运用决策树法描述考虑交叉竞争博弈下的DEA评价与选择过程;变评价过程中效用值改变的途径由“基于权重的交换”转化为“基于交叉竞争博弈的指标值调整”,实施对DEA模型的改进,设计交叉竞争的博弈效率DEA评价方法,得出确定型、风险型和不确定型DEA方法的分类和交叉竞争的博弈效率评价过程;从经济学的博弈论和管理学的决策分析来解释DEA,实现更加直观的DMU评价过程和更符合客观实际的评价情景.最后通过算例验证所提出方法的可行性、有效性和保序性.     

边缘计算中基于博弈论的数据协作缓存策略研究

针对边缘计算中服务器存储能力有限的问题,提出一种基于博弈论的数据协作缓存策略。该策略根据基站覆盖范围将边缘计算环境划分为多个区域,每个区域与相邻区域协作缓存数据资源。在每个区域中,计算每个数据块对本地区域及相邻区域的缓存价值,根据待缓存资源的缓存价值进行缓存决策,最小化用户获取数据资源的延迟。仿真实验结果表明,提出缓存策略比现有非协作缓存策略数据资源平均获取延迟降低了36.55%,有效降低了数据资源平均获取延迟。     

A cooperative game theory based user-centered medical device design decision approach under uncertainty

The medical device conceptual design decision-making is a process of coordinating pertinent stakeholders, which will significantly affect the quality of follow-up market competitiveness. However, as the most challenging parts of user-centered design, traditional methods are mainly focusing on determining the priorities of the evaluation criteria and forming the comprehensive value (utility) of the conceptual scheme, may not fully deal with the interaction and interdependent between the conflicts of interest among stakeholders and weigh the ambiguous influence on the overall design expectations, which results in the unstable decision-making results. To overcome this drawback, this paper proposes a cooperative game theory based decision model for device conceptual scheme under uncertainty. The proposed approach consists of three parts: first part is to collect and classify needs of end users and professional users based on predefined evaluation criteria; second part is using rough set theory technique to create criteria correlation diagram and scheme value matrix from users; and third part is developing the fuzzy coalition utility model to maximize the overall desirability through the criteria correlation diagram with the conflict of interests of end and professional users considered, and then selecting the optimal scheme. A case study of blood pressure meter is used to illustrate the proposed approach and the result shows that this approach is more robust compared with the widely used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach.     

A cooperative game coding scheme

The transmission of a message over a Gaussian channel using a noisy feedback and signals with no bandwidth constraint can be treated as a two-person cooperative game with one of the players stationary. The resultant coding strategy allows error-free transmission at a rate R < R_c, the critical signalling rate. An expression for the probability of error is developed for finite values of the transmission interval T. In the case of noiseless feedback, this coding scheme emerges as a generalization of the scheme of Schalkwijk and Kailath.     

Energy-efficient power allocation for selfish cooperative communication networks using bargaining game

In commercial networks,user nodes operating on batteries are assumed to be selfish to consume their energy solely to maximize their own benefits,e.g.,data rates.In this paper,we propose a bargaining game to perform the power allocation for the selfish cooperative communication networks.In our system,two partner nodes can act as a source as well as a relay for each other,and each node is with an energy constraint to transmit one frame.Consider a selfish node is willing to seek cooperative transmission only if the data rate achieved through cooperation will not lower than that achieved through noncooperation by using the same amount of energy.The energy-efficient power allocation problem can be modeled as a cooperative game.We proved that there exists a unique Nash bargaining solution (NBS) for the game by verifying that the game is indeed a bargaining problem.Thus,the two objectives,i.e.,system efficiency and user fairness specified in the selfish networks can be achieved.Simulation results show that the NBS scheme is efficient in that the performance loss of the NBS scheme to that of the maximal overall rate scheme is small while the maximal-rate scheme is unfair.The simulation results also show that the NBS result is fair in that both nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance.     

A cooperative Homicidal Chauffeur game

We address a pursuit-evasion problem involving an unbounded planar environment, a single evader and multiple pursuers moving along curves of bounded curvature. The problem amounts to a multi-agent version of the classic Homicidal Chauffeur problem; we identify parameter ranges in which a single pursuer is not sufficient to guarantee evader capture. We propose a novel multi-phase cooperative strategy in which the pursuers move in specific formations and confine the evader to a bounded region. The proposed strategy is inspired by the hunting and foraging behaviors of various fish species. We characterize the required number of pursuers for which our strategy is guaranteed to lead to confinement.     

Ranking efficient decision-making units in data envelopment analysis using fuzzy concept

This paper introduces a new mathematical method for improving the discrimination power of data envelopment analysis and to completely rank the efficient decision-making units (DMUs). Fuzzy concept is utilised. For this purpose, first all DMUs are evaluated with the CCR model. Thereafter, the resulted weights for each output are considered as fuzzy sets and are then converted to fuzzy numbers. The introduced model is a multi-objective linear model, endpoints of which are the highest and lowest of the weighted values. An added advantage of the model is its ability to handle the infeasibility situation sometimes faced by previously introduced models.     

Conflict resolution using game theory and rough sets

Conflicts occur naturally in the real world at all levels of society, individually, in groups or society as a whole. Almost all the existing conflict resolution models are unilateral in their decision‐making process. They do not consider the actions of the involved parties simultaneously. Therefore, in this paper, we aim to design a novel conflict resolution model based on game‐theoretic rough sets by constructing a game between all the concerned parties (players), computing the payoff of different strategies and classifying them following equilibrium rules. The proposed model yields more realistic and accurate results as it explores all possibilities and is flexible in determining different threshold values relative to the complexities of real‐life problems. Three real‐life conflict situations are solved with the proposed model, and a comprehensive analysis is done to validate the effectiveness of the proposed approach.     

Iris recognition using shape-guided approach and game theory

Most state-of-the-art iris recognition algorithms claim to perform with a very high recognition accuracy in a strictly controlled environment. However, their recognition accuracies significantly decrease when the acquired images are affected by different noise factors including motion blur, camera diffusion, head movement, gaze direction, camera angle, reflections, contrast, luminosity, eyelid and eyelash occlusions, and problems due to contraction and dilation. The novelty of this research effort is that we propose to apply a variational model to localize the iris region belonging to given shape space using active contour method, a geometric shape prior, and the Mumford–Shah functional. This variational model is robust against noise, poor localization and weak iris/sclera boundaries. Furthermore, we apply the Modified Contribution-Selection Algorithm (MCSA) for iris feature ranking based on the Multi-Perturbation Shapley Analysis (MSA), a framework which relies on cooperative game theory to estimate the effectiveness of the features iteratively and select them accordingly, using either forward selection or backward elimination approaches. The verification and identification performance of the proposed scheme is validated using the ICE 2005, the UBIRIS Version 1, the CASIA Version 3 Interval, and WVU Nonideal datasets.     

Partial syndrome-based system-level fault diagnosis using game theory

This paper introduces a novel diagnosis approach, using game theory, to solve the comparison-based system-level fault identification problem in distributed and parallel systems based on the asymmetric comparison model. Under this diagnosis model tasks are assigned to pairs of nodes and the results of executing these tasks are compared. Using the agreements and disagreements among the nodes’ outputs, i.e. the input syndrome, the fault diagnosis algorithm identifies the fault status of the system’s nodes, under the assumption that at most t of these nodes can permanently fail simultaneously. Since the introduction of the comparison model, significant progress has been made in both theory and practice associated with the original model and its offshoots. Nevertheless, the problem of efficiently identifying the set of faulty nodes when not all the comparison outcomes are available to the fault identification algorithm prior to initiating the diagnosis phase, i.e. partial syndromes, remains an outstanding research issue. In this paper, we first show how game theory can be adapted to solve the fault diagnosis problem by maximising the payoffs of all players (nodes). We then demonstrate, using results from a thorough simulation, the effectiveness of this approach in solving the fault identification problem using partial syndromes from randomly generated diagnosable systems of different sizes and under various fault scenarios. We have considered large diagnosable systems, and we have experimented extreme faulty situations by simulating all possible fault sets even those that are less likely to occur in practice. Over all the extensive simulations we have conducted, the new game-theory-based diagnosis algorithm performed very well and provided good diagnosis results, in terms of correctness, latency, and scalability, making it a viable addition or alternative to existing diagnosis algorithms.     

Capacity sharing in a network of independent factories: A cooperative game theory approach

Market changes, uncertainties and unpredictability characterize the market environment nowadays. The possibility to cooperate in a network allows companies to stay competitive. This paper proposes a game theory coordination mechanism for a network of independent plants; the approach is based on the OWEN’s theorem properly simulated in order to investigate different performances in various environmental conditions. To support the network and face the capacity sharing issue, a Multi-Agent Architecture has been specifically developed. The simulation results show that the proposed mechanism could be considered a valid alternative to the more usual negotiation approaches in several market conditions. The plants gain considerable benefits by participating in a network supported by the cooperative mechanism proposed.     

EDGly: detection of influential nodes using game theory

Identifying those nodes that play a critical role within a network is of great importance. Many applications such as gossip spreading, disease spreading, news dispersion, identifying prominent individuals in a social network, etc. may take advantage of this knowledge in a complex network. The basic concept is generally to identify the nodes with the highest criticality in a network. As a result, the centrality principle has been studied extensively and in great detail, focusing on creating a consistent and accurate location of nodes within a network in terms of their importance. Both single centrality measures and group centrality measures, although, have their certain drawbacks. Other solutions to this problem include the game-theoretic Shapley Value (SV) calculations measuring the effect of a collection of nodes in complex networks via dynamic network data propagation process. Our novel proposed algorithm aims to find the most significant communities in a graph with community structure and then employs the SV-based games to find the most influential node from each community. A Susceptible-Infected-Recovered (SIR) model has been employed to distinctly determine each powerful node's capacity to spread. The results of the SIR simulation have also been used to show the contrast between the spreading capacity of nodes found through our proposed algorithm and that of nodes found using SV-algorithm and centrality measures alone.

     

煤矿井下基于博弈论CR节点协作通信研究

现有无线通信系统在不同矿区通用性不强、通信效果不佳。煤矿井下认知系统可通过对煤矿井下通信环境参数的感知,对感知参数信息的融合、分析,利用动态博弈理论实现动态的频率选择、功率分配、调制方式等参数的调整,获得有效的通信效果和不同矿区兼容。通过Matlab进行了仿真验证,仿真结果表明：该算法能够快速收敛于系统稳定状态,而且,复杂度低,易于实现。     

On the inclusion of macroscopic theory in Monte Carlo simulation using game theory

This paper presents the inclusion of macroscopic damage theory into Monte Carlo particle-range simulation using game theory. A new computer code called RADDI was developed on the basis of this inclusion. Results of Monte Carlo damage simulation after 6.3 MeV proton bombardment of silicon are compared with experimental data of Bulgakov et al.     

Task graph pre-scheduling, using Nash equilibrium in game theory

Prescheduling algorithms are targeted at restructuring of task graphs for optimal scheduling. Task graph scheduling is a NP-complete problem. This article offers a prescheduling algorithm for tasks to be executed on the networks of homogeneous processors. The proposed algorithm merges tasks to minimize their earliest start time while reducing the overall completion time. To this end, considering each task as a player attempting to reduce its earliest time as much as possible, we have applied the idea of Nash equilibrium in game theory to determine the most appropriate merging. Also, considering each level of a task graph as a player, seeking for distinct parallel processors to execute each of its independent tasks in parallel with the others, the idea of Nash equilibrium in game theory can be applied to determine the appropriate number of processors in a way that the overall idle time of the processors is minimized and the throughput is maximized. The communication delay will be explicitly considered in the comparisons. Our experiments with a number of known benchmarks task graphs and also two well-known problems of linear algebra, LU decomposition and Gauss–Jordan elimination, demonstrate the distinguished scheduling results provided by applying our algorithm. In our study, we consider ten scheduling algorithms: min–min, chaining, A ^?, genetic algorithms, simulated annealing, tabu search, HLFET, ISH, DSH with task duplication, and our proposed algorithm (PSGT).