面向关键服务安全态势的定量评估模型

针对关键服务安全态势量化分析难的问题,设计并实现一种面向关键服务安全态势的定量评估模型。该模型将服务可生存性和服务性能指标作为反映服务变化内因和外因,综合考虑性能指标来表征服务在一定可生存性下的外在表现形式。利用域名系统服务对该模型的验证结果表明,该模型能完成对服务安全状态的量化分析和态势生成,有效检测服务的偏离和失效,为安全管理员正确决策提供支持。     

五道棋计算机博弈程序的设计与实现

针对民间五道棋博弈游戏,设计并实现五道棋计算机博弈程序。介绍程序的功能、设计目标,给出程序中关键问题的处理方法和软件实现方法。经实际应用证明,五道棋计算机博弈程序运行速度快、稳定可靠。该博弈程序的设计与实现对完成其他民间棋类的计算机博弈程序具有一定的参考价值。     

A fuzzy approach to strategic games

A game is a decision-making situation with many players, each having objectives that conflict with each other. The players involved in the game usually make their decisions under conditions of risk or uncertainty. In the paper, a fuzzy approach is proposed to solve the strategic game problem in which the pure strategy set for each player is already defined. Based on the concepts of fuzzy set theory, the approach uses a multicriteria decision-making method to obtain the optimal strategy in the game, a method which shows more advantages than the classical game methods. Moreover, with this approach, some useful conclusions are reached concerning the famous “prisoner's dilemma” problem in game theory     

Interval-valued intuitionistic fuzzy matrix games based on Archimedean t-conorm and t-norm

Fuzzy game theory has been applied in many decision-making problems. The matrix game with interval-valued intuitionistic fuzzy numbers (IVIFNs) is investigated based on Archimedean t-conorm and t-norm. The existing matrix games with IVIFNs are all based on Algebraic t-conorm and t-norm, which are special cases of Archimedean t-conorm and t-norm. In this paper, the intuitionistic fuzzy aggregation operators based on Archimedean t-conorm and t-norm are employed to aggregate the payoffs of players. To derive the solution of the matrix game with IVIFNs, several mathematical programming models are developed based on Archimedean t-conorm and t-norm. The proposed models can be transformed into a pair of primal–dual linear programming models, based on which, the solution of the matrix game with IVIFNs is obtained. It is proved that the theorems being valid in the exiting matrix game with IVIFNs are still true when the general aggregation operator is used in the proposed matrix game with IVIFNs. The proposed method is an extension of the existing ones and can provide more choices for players. An example is given to illustrate the validity and the applicability of the proposed method.     

结合先验知识与蒙特卡罗模拟的麻将博弈研究

针对内陆麻将缺乏统一平台和大量牌谱数据,难以设计出基于监督学习的博弈算法的问题,本文设计了一系列将规则、经验与蒙特卡罗方法相结合的博弈算法.首先,分别针对麻将博弈的弃牌模块、听牌模块、吃牌模块提出了弃牌优先级、听牌有效数、吃牌优先级的方法,完善了麻将AI的知识体系,设计了基础版博弈算法Fanfou_ba和优化版博弈算法...     

A graph-based representation of game scenarios; methodology for minimizing anomalies in computer game

When we design the computer games, new concepts are hard to communicate because the development of the new computer games is far less formalized compared with other software. However, the creative processing additionally has the benefit if the new concepts of the computer games are widely used and discussed. We focus on understanding the ideas to be shared between the game designer and the game programmer in the game design. The research defines a graph-based representation of game scenarios is in terms of Event graph, State graph, and Action graph forms in order to minimize anomalies of game flow design. This method in our research can reduce unexpected and undesirable game situations. Also it reduces the mismatched information between the game designer and the game programmer in game-playing. Compared with the existing method such as storyboard, flowchart, and SSM, we demonstrate the viability of our game design methodology.     

智能博弈对抗中的对手建模方法及其应用综述

智能博弈对抗一直是人工智能研究的热点。在博弈对抗环境中,通过对对手进行建模,可以推测敌对智能体动作、目标、策略等相关属性,为博弈策略制定提供关键信息。对手建模方法在竞技类游戏和作战仿真推演等领域的应用前景广阔,博弈策略的制定必须以博弈各方的行动策略为前提,因此建立一个准确的对手行为模型对于预测其意图尤其重要。从内涵、方法、应用三个方面,阐述了对手建模的必要性,对现有建模方式进行了分类;对基于强化学习的预测方法、基于心智理论的推理方法和基于贝叶斯的优化方法进行了梳理与总结;以序贯博弈（德州扑克）、即时策略博弈（星际争霸）和元博弈为典型应用场景,分析了智能博弈对抗过程中的对手建模的作用;从有限理性、策略欺骗性和可解释性三个方面进行了对手建模技术发展的展望。     

A game theoretic approach for the distributed control of multi-agent systems under directed and time-varying topology

This paper presents a new game theory based method to control multi-agent systems under directed and time varying interaction topology. First, the sensing/communication matrix is introduced to cope with information sharing among agents, and to provide the minimal information requirement which ensures the system level objective is desirable. Second, different from traditional methods of controlling multi-agent systems, non-cooperative games are investigated to enforce agents to make rational decisions. And a new game model, termed stochastic weakly acyclic game, is developed to capture the optimal solution to the distributed optimization problem for multi-agent systems with directed topology. It is worth noting that the system level objective can be achieved at the points of the corresponding equilibriums of the new game model. The proposed method is illustrated with an example in smart grid where multiple distributed generators are controlled to reach the fair power utilization profile in the game formulation to ensure the aggregated power output are optimal.     

Matrix games with interval data

The conventional game theory is concerned with how rational individuals make decisions when they are faced with known payoffs. In the real world, sometimes the payoffs are not known and have to be estimated, and sometimes the payoffs are only approximately known. This paper develops a solution method for the two-person zero-sum game where the payoffs are imprecise and are represented by interval data. Since the payoffs are imprecise, the value of the game should be imprecise as well. A pair of two-level mathematical programs is formulated to obtain the upper bound and lower bound of the value of the game. Based on the duality theorem and by applying a variable substitution technique, the pair of two-level mathematical programs is transformed to a pair of ordinary one-level linear programs. Solving the pair of linear programs produces the interval of the value of the game. It is shown that the two players in the game have the same upper bound and lower bound for the value of the imprecise game. An example illustrates the whole idea and sheds some light on imprecise game.     

Multi-objective design method based on evolution game and its application for suspension

Through research and bionics of biology survival mode, game players with competition, cooperation and self-adaptation capacity are introduced in the multi-objective design. The dynamic behavior and bounded rationality in game processes for players are considered according to Chinese saying “In success, commit oneself to the welfare of the society; in distress, maintain one’s own integrity”. An evolution rule, Poor-Competition-Rich-Cooperation (short for PCRC), is proposed. Then, the corresponding payoff functions of competition and cooperation behavior are established and a multi-objective design method based on evolution game is proposed. The calculation steps are as follows: 1) Taking the design objectives as different game players, and calculating factors of the design variables to objective and fuzzy clustering. The design variables are divided into multiple strategy subsets owned by each game player. 2) According to the evolution rule, each player determines its behavior and payoff function in this game round. 3) In their own strategy subsets, each game player takes their payoff as mono-objective for optimization. It gives the best strategy upon other players. And so the best strategies of all players conform the group strategy in this round. The final equilibrium solution is obtained through multi-round game based on convergence criterion. The validity and reliability of this method are shown by the results of an example of a tri-objective optimization design of passive suspension parameters.     

一种围棋定式的机器学习方法

提出了一种围棋定式的机器学习方法。利用此方法可实现从棋谱库中自动提取定式并生成定式库。此外，对于棋谱数量较大的情况，采用分阶段学习方法，提高了学习效率。应用此方法，时34000局棋谱进行处理，得到定式点680638个。最后，还给出了1种基于组合博弈理论在计算机围棋博弈系统中使用定式的方法。     

中国象棋人机对弈的自学习方法研究

机器博弈被认为是人工智能领域最具挑战性的研究方向之一。中国象棋计算机博弈的难度绝不亚于国际象棋，但是涉足学者太少，具有自学习能力的就更少了。介绍了中国象棋人机对弈原理，给出了近年来几类典型的评估函数学习方法及其原理，通过比较得出了最适合中国象棋使用的学习方法。分析了这些方法尚存在的问题，并提出了未来研究方向。     

中国象棋人机对弈的自学习方法研究

机器博弈被认为是人工智能领域最具挑战性的研究方向之一。中国象棋计算机博弈的难度绝不亚于国际象棋,但是涉足学者太少,具有自学习能力的就更少了。介绍了中国象棋人机对弈原理,给出了近年来几类典型的评估函数自学习方法及其原理,通过比较得出了最适合中国象棋使用的学习方法。分析了这些方法尚存在的问题,并提出了未来的研究方向。     

全局游戏策略中基于经验的学习方法

全局游戏策略GGP(General Game Playing)旨在开发一种没有游戏经验支撑下能够精通各类游戏的人工智能。在原有强化学习算法研究的基础上,提出一种基于经验的简化学习方法,通过对游戏状态的筛选和游戏经验的归纳,从而降低决策对经验数量的需求,提高决策效率,并能达到指定胜利、平局或失败的游戏目标。通过在三种不同的游戏规则下与玩家进行游戏比赛实验表明,该学习方法能有效地达到预期结果。     

Resolving-Functions Method for Game Problems of Control with Integral Constraints

The method of resolving functions is investigated as applied to game problems of control with integral constraints. A scheme of the method is proposed that ensures the end of a game within a definite guaranteed time in the class of stroboscopic strategies. The results of comparison of the guaranteed times of this scheme of the resolving-functions method with those of the first direct Pontryagin method for integral constraints are shown.     

基于稀疏贝叶斯算法的演化博弈网络重构

演化博弈是自然和社会系统中一种常见的互动类型,探知演化博弈网络的拓扑结构是理解其功能和集体行为的基础。对于演化博弈网络,个体的博弈行为通常难以用动力学方程进行描述,而且相关的时序信息一般数量有限并且是离散的,因此在有限的个体博弈信息下重构网络的结构有着重要的研究意义。本文基于稀疏贝叶斯学习方法进一步发展了演化博弈网络的重构方法,通过在随机网络和小世界网络上的数值模拟验证该方法的有效性。与先前的基于L1范数的方法相比,该方法同样能够在较少的个体博弈信息下实现网络的重构,并且具有更高的重构效率和更强的噪声鲁棒性。     

Decentralised zero-sum differential game for a class of large-scale interconnected systems via adaptive dynamic programming

In this paper, a novel decentralised differential game strategy for large-scale nonlinear systems with matched interconnections is developed by using adaptive dynamic programming technique. First, the Nash-equilibrium solutions of the corresponding isolated differential game subsystems are found by appropriately redefining the associated cost functions accounting for the bounds of interconnections. Then, the decentralised differential game strategy is established by integrating all the modified Nash-equilibrium solutions of the isolated subsystems to stabilise the overall system. Next, the solutions of Hamilton–Jacobi–Isaaci equations are approximated online by constructing a set of critic neural networks with adaptation law of weights. The stability analysis of each subsystem is provided to show that all the signals in the closed-loop system are guaranteed to be bounded by utilising Lyapunov method. Finally, the effectiveness of the proposed decentralised differential game method is illustrated by a simple example.     

采用时间差分算法的九路围棋机器博弈系统

围棋机器博弈是机器博弈中重要的分支之一,其庞大的博弈空间给机器博弈研究者带来了巨大挑战.目前围棋机器博弈多采用静态估值搜索与蒙特卡洛树搜索,故将时间差分算法引入至九路围棋机器博弈系统中,提出基于时间差分算法的围棋机器博弈系统模型,该博弈系统具有一定的自学习能力,能在不断的对弈中逐步提高博弈能力.通过与采用α-β搜索算法的博弈系统进行实际对弈,证明了该方法的可行性.     

闯关游戏式的大学课程教学考核方法研究

提出了一种基于闯关游戏式的课程教学考核方法：将课程知识融入到闯关式的游戏场景中,学生通过独立自主的游戏闯关完成课程考核,并获得综合客观的成绩评估。阐述了闯关游戏式考核的基本理念,并详细讨论了具体的方案设计,通过分析得出该考核方法具有优势的结论。     

博弈控制论简述

博弈控制论是近年来出现的博弈与控制的交叉学科,虽然它融合了博弈和控制双方的工具和方法,但无论从研究对象,研究方法与现有的结论看,它都不完全从属于经典博弈或经典控制的范畴,因此,是一个具有自身特色的新学术生长点.本文的目的是对这个新方向做一个简述介绍,内容包括:i)基于博弈的控制;ii)基于势博弈的优化;iii)博弈中的状态空间方法;iv)国内几个研究博弈控制论的团队.最后,对博弈控制论的未来做一展望.