模糊联盟图合作对策tau值

针对模糊环境下有限制的联盟合作情况,利用Choquet积分定义了模糊联盟图合作对策tau值,证明了其存在性和其他重要性质,讨论了其与模糊核心的关系,并给出凸模糊联盟图合作对策tau值的计算公式.最后通过一个算例表明了该tau值的有效性和合理性.研究发现,该tau值是传统tau值的拓展.     

具有交流结构的区间模糊联盟合作对策

当前具有模糊联盟的合作对策研究主要基于任意局中人可自由结盟的基本假设,但现实结盟活动中,局中人普遍受到资源或地位等因素的限制,其合作往往具有交流结构限制。因此,基于Choquet模糊延拓研究了具有交流结构的区间模糊多人合作对策,给出了相应区间模糊联盟平均树解,并通过公理化体系对此解进行了研究。通过供应链纵向合作创新收益分配实例应用,并与区间模糊联盟合作对策的Shapley值进行比较,说明该方法的现实有效性。     

具有区间模糊联盟的带风险偏好图合作对策A-T解

当前模糊合作对策研究主要基于局中人无差异且联盟组建无约束假设,但现实联盟组建普遍具有限制约束性.针对具有限制交流结构的模糊联盟合作问题,考虑局中人具有不同的偏好差异,提出一种基于风险偏好均值的模糊联盟图合作对策及其A-T解,并公理化论证解的存在性.考虑风险偏好不仅可以体现局中人行为差异性,而且利于模糊联盟支付函数求解.最后通过实例表明了所提出方法的现实有效性和可行性.     

直觉模糊集的Choquet积分相关测度及其决策应用

Qu给出的直觉模糊集的Choquet积分相关系数的计算公式与相关系数的性质相矛盾.为此,通过一个实例说明Qu定义的直觉模糊Choquet积分相关系数定义存在的问题,并结合相关系数的性质证明,分析问题出现的原因;然后,针对存在的问题,以直觉模糊集的Choquet积分相关指标为基础,给出新的直觉模糊集的Choquet积分信息能量的概念,定义新的直觉模糊集的Choquet积分相关系数,并讨论相关系数的性质;最后,利用新定义的直觉模糊集的Choquet积分相关测度,推导出方案与正理想方案之间的Choquet积分相关系数计算公式,据此提出一种直觉模糊多属性决策方法,并通过实例分析以及方法对比,说明所提出方法的可行性和有效性.     

模糊网络博弈的合作联盟优化对策研究

模糊网络博弈主要关注如何将联盟收益分配给合作联盟的每个参与者,其广义模糊博弈解同时引入参与度和调整系数,不仅实现了参与者以部分资源参与合作联盟的愿望,而且满足了保留部分收益值用于联盟再发展的需求.本文作为模糊博弈模型的后续深入研究,对模糊网络博弈的解进行拓展,提出广义模糊核心解、广义模糊谈判集解的概念,并证明当满足超可加性的前提下,模糊网络博弈的广义模糊核心解与其广义模糊谈判集解具有等价关系,并刻画了模糊网络博弈广义核心解的非空性,算例分析结果表明合作联盟广义分配方案的存在性,为合作联盟优化对策提供服务.     

准则关联的直觉模糊多准则决策方法

针对准则值为直觉三角模糊数,准则间相互关联的多准则决策问题,提出基于Choquet分的决策方法．该方法首先利用偏好函数定义方案在各准则下的优序关系,若模糊测度已知,则直接利用Choquet积分进行求解;若准则集上的模糊测度未知,则利用部分决策信息和最小方差法建立二次规划模型,求解模糊测度,再利用Choquet分进行决策．最后通过实例表明了该方法的有效性和可行性．     

一种要素双模糊的限制交流结构合作博弈方法及应用

针对具有限制交流结构的合作博弈,基于现实结盟局中人参与联盟程度模糊且联盟支付模糊的情形,提出一种带局中人偏好的要素(联盟与支付)双模糊限制交流结构合作博弈的A-T解(average tree solution)分配方法.首先基于局中人的风险偏好均值,结合模糊联盟合作博弈的Choquet积分形式,构建要素双模糊合作联盟的特征函数,进而给出一个满足公理体系的解公式.该博弈模型不仅能够刻画现实结盟的约束性和模糊性,且有利于分配收益函数的求解.最后,通过对典型限制交流农地经营PPP合作项目收益分配问题的应用分析,表明所提出方法相对于Shapley值更具合理性.     

多决策树的模糊积分融合在银行信贷管理系统中的应用

为了提高基于数据挖掘的商业银行信贷管理系统的信贷风险评估水平,将多决策树的Choquet模糊积分融合(MTCFF)模型应用到银行信贷管理系统中。基本思想是采用决策树在已知类型的客户数据上进行挖掘,按照决策树剪枝程度不同形成不同的决策树并产生规则,利用所生成的不同决策树的规则,对未知类型的客户数据进行分类,然后让Choquet模糊积分对多棵决策树的分类结果进行融合,形成最优判断。采用UCI数据库中German客户信用卡数据集进行验证,实验证明Choquet模糊积分的非线性融合效果优于单棵决策树的分类效果,也优于其他线性融合方法,并且Choquet模糊积分要优于Sugeno模糊积分。     

Choquet积分的模糊化扩展II型

重点讨论Choquet积分的II型模糊化扩展形式。相对于Choquet积分的模糊化扩展I型,II型扩展支持模糊数的被积函数,并给出精确数的积分结果。基于带符号的模糊测度,分别讨论了Choquet积分的模糊化扩展II型的计算方法和相关算法。并用实例说明:Choquet积分的模糊化扩展II型作为一种聚合工具在处理涉及非精确数的数据挖掘问题(例如:推理和回归问题)上的实用价值。     

多决策树的模糊积分融合在银行信贷管理系统中的应用

为了提高基于数据挖掘的商业银行信贷管理系统的信贷风险评估水平,将多决策树的Choquet模糊积分融合(MTCFF)模型应用到银行信贷管理系统中。基本思想是采用决策树在已知类型的客户数据上进行挖掘,按照决策树剪枝程度不同形成不同的决策树并产生规则,利用所生成的不同决策树的规则,对未知类型的客户数据进行分类,然后让Choquet模糊积分对多棵决策树的分类结果进行融合,形成最优判断。采用UCI数据库中German客户信用卡数据集进行验证,实验证明Choquet模糊积分的非线性融合效果优于单棵决策树的分类效果,也优于其他线性融合方法,并且Choquet模糊积分要优于Sugeno模糊积分。     

Strongly Subgame-Consistent Core in Stochastic Games

This paper investigates stochastic games on finite tree graphs. A given n-player normal-form game is defined at each node of a tree. Transition to a next node of the tree is random and depends on the strategy profile realized in a current game. We construct a cooperative solution of the game by maximizing the total expected payoff of the players. The core is used as the solution concept of the cooperative game. We introduce the definition of a strongly subgame-consistent (strongly time-consistent) core. Finally, we suggest a method for designing a cooperative distribution procedure of an imputation from the core that guarantees its strong subgame consistency.     

Cooperative games with multiple attributes

ABSTRACT

In this paper, we introduce the notion of a cooperative game with multiple attributes where players can provide partial participations in multiple attributes and form coalitions. The power or influence of the players due to their multiple attributes is evaluated based on their memberships in the coalitions. Our game therefore, extends the notion of cooperative games with fuzzy coalitions. The Shapley function for this class of games is proposed as a rational and fair solution concept. Every fuzzy game stems out of a specific crisp game under the assumption that the players provide partial memberships in forming multiple coalitions simultaneously. We adopt similar techniques to obtain the cooperative games with multiple attributes from their crisp counterparts and subsequently determine their Shapley functions.     

Internal structure of coalitions in competitive and altruistic graphical coalitional games

This paper introduces a certain graphical coalitional game where the internal topology of the coalition depends on a prescribed communication graph structure among the agents. The game Value Function is required to satisfy four Axioms of Value. These axioms make it possible to provide a refined study of coalition structures on graphs by defining a formal graphical game and by assigning a Positional Advantage, based on the Shapley value, to each agent in a coalition based on its connectivity properties within the graph. Using the Axioms of Value the graphical coalitional game can be shown to satisfy properties such as convexity, fairness, cohesiveness, and full cooperativeness. Three measures of the contributions of agents to a coalition are introduced: marginal contribution, competitive contribution, and altruistic contribution. The mathematical framework given here is used to establish results regarding the dependence of these three types of contributions on the graph topology, and changes in these contributions due to changes in graph topology. Based on these different contributions, three online sequential decision games are defined on top of the graphical coalitional game, and the stable graphs under each of these sequential decision games are studied. It is shown that the stable graphs under the objective of maximizing the marginal contribution are any connected graph. The stable graphs under the objective of maximizing the competitive contribution are the complete graph. The stable graphs under the objective of maximizing the altruistic contribution are any tree.     

Games Played under Fuzzy Constraints

Psychological experiment studies reveal that human interaction behaviors are often not the same as what game theory predicts. One of important reasons is that they did not put relevant constraints into consideration when the players choose their best strategies. However, in real life, games are often played in certain contexts where players are constrained by their capabilities, law, culture, custom, and so on. For example, if someone wants to drive a car, he/she has to have a driving license. Therefore, when a human player of a game chooses a strategy, he/she should consider not only the material payoff or monetary reward from taking his/her best strategy and others' best responses but also how feasible to take the strategy in that context where the game is played. To solve such a game, this paper establishes a model of fuzzily constrained games and introduces a solution concept of constrained equilibrium for the games of this kind. Our model is consistent with psychological experiment results of ultimatum games. We also discuss what will happen if Prisoner's Dilemma and Stag Hunt are played under fuzzy constraints. In general, after putting constraints into account, our model can reflect well the human behaviors of fairness, altruism, self‐interest, and so on, and thus can predict the outcomes of some games more accurate than conventional game theory.     

Infinite games on finitely coloured graphs with applications to automata on infinite trees

We examine a class of infinite two-person games on finitely coloured graphs. The main aim is to construct finite memory winning strategies for both players. This problem is motivated by applications to finite automata on infinite trees. A special attention is given to the exact amount of memory needed by the players for their winning strategies. Based on a previous work of Gurevich and Harrington and on subsequent improvements of McNaughton we propose a unique framework that allows to reestablish and to improve various results concerning memoryless strategies due to Emerson and Jutla, Mostowski, Klarlund.     

A self-learning fuzzy approach for proactive resource provisioning in cloud environment

The development of a communication infrastructure has made possible the expansion of the popular massively multiplayer online games. In these games, players all over the world can interact with one another in a virtual environment. The arrival rate of new players to the game environment causes fluctuations and players always expect services to be available and offer an acceptable service-level agreement (SLA), especially in terms of response time and cost. Cloud computing emerged in the recent years as a scalable alternative to respond to the dynamic changes of the workload. In massively multiplayer online games applications, players are allowed to lease resources from a cloud provider in an on-demand basis model. Proactive management of cloud resources in the face of workload fluctuations and dynamism upon the arrival of players are challenging issues. This paper presents a self-learning fuzzy approach for proactive resource provisioning in cloud environment, where key is to predict parameters of the probability distribution of the incoming players in each period. In addition, we propose a self-learning fuzzy autoscaling decision-maker algorithm to compute the proper number of resources to be allocated to each tier in the massively multiplayer online games by applying the predicted workload and user SLA. We evaluate the effectiveness of the proposed approach under real and synthetic workloads. The experimental results indicate that the proposed approach is able to allocate resources more efficiently than other approaches.     

Types of arcs in a fuzzy graph

The concept of connectivity plays an important role in both theory and applications of fuzzy graphs. Depending on the strength of an arc, this paper classifies arcs of a fuzzy graph into three types namely α-strong, β-strong and δ-arcs. The advantage of this type of classification is that it helps in understanding the basic structure of a fuzzy graph completely. We analyze the relation between strong paths and strongest paths in a fuzzy graph and obtain characterizations for fuzzy bridges, fuzzy trees and fuzzy cycles using the concept of α-strong, β-strong and δ-arcs. An arc of a fuzzy tree is α-strong if and only if it is an arc of its unique maximum spanning tree. Also we identify different types of arcs in complete fuzzy graphs.