Dynamic Game Theoretic Model of Multi-Layer Infrastructure Networks

Due to similarities in terms of network structure and interactions among them, most infrastructure systems can be viewed as coupled layers of a generalized transportation network in which the passenger, freight, data, water, and energy flows are the commodities in the different layers. The coupling is due to the varying degrees of interactions among these layers in terms of shared physical networks, budgetary constraints, socio-economic environments, environmental concerns, information/other resources, and in particular, functional interdependencies. However, these interactions are normally ignored in the engineering planning, design and analysis of infrastructure systems. Identifying and understanding these interactions using a holistic perspective can lead to more efficient infrastructure systems. This paper presents a preliminary network flow equilibrium model of dynamic multi-layer infrastructure networks in the form of a differential game involving two essential time scales. In particular, three coupled network layers—automobiles, urban freight and data—are modeled as being comprised of Cournot-Nash dynamic agents. An agent-based simulation solution structure is introduced to solve the flow equilibrium and optimal budget allocation problem for these three layers under the assumption of a super authority that oversees investments in the infrastructure of all three technologies and thereby creates a dynamic Stackelberg leader-follower game.     

A Comparative Study of Game Theoretic and Evolutionary Models of Bargaining for Software Agents

Most of the existing work in the study of bargaining behavior uses techniques from game theory. Game theoretic models for bargaining assume that players are perfectly rational and that this rationality is common knowledge. However, the perfect rationality assumption does not hold for real-life bargaining scenarios with humans as players, since results from experimental economics show that humans find their way to the best strategy through trial and error, and not typically by means of rational deliberation. Such players are said to be boundedly rational. In playing a game against an opponent with bounded rationality, the most effective strategy of a player is not the equilibrium strategy but the one that is the best reply to the opponents strategy. The evolutionary model provides a means for studying the bargaining behaviour of boundedly rational players. This paper provides a comprehensive comparison of the game theoretic and evolutionary approaches to bargaining by examining their assumptions, goals, and limitations. We then study the implications of these differences from the perspective of the software agent developer.     

A Game Theoretic Approach for a Minimal Secure Dominating Set

The secure dominating set (SDS), a variant of the dominating set, is an important combinatorial structure used in wireless networks. In this paper, we apply algorithmic game theory to study the minimum secure dominating set (MinSDS) problem in a multi-agent system. We design a game framework for SDS and show that every Nash equilibrium (NE) is a minimal SDS, which is also a Pareto-optimal solution. We prove that the proposed game is an exact potential game, and thus NE exists, and design a polynomial-time distributed local algorithm which converges to an NE in O (n) rounds of interactions. Extensive experiments are done to test the performance of our algorithm, and some interesting phenomena are witnessed.

     

Towards A Game Theoretic Understanding of Ad-Hoc Routing

In this paper we present a new application of game theory, in which game theoretic techniques are used to provide a rigorous underpinning to the analysis of ad-hoc routing protocols. The explosion of interest in ad-hoc networks over the last few years has resulted in a very large number of routing protocols being proposed. Despite this, the science of analysing routing protocols is still relatively immature, and the question that remains is how to decide “how good” a given protocol is. We propose a game theoretic approach as a potentially effective means of answering this question. The conceptual mapping of routing into a game is, we believe, natural and simple. Furthermore, game theory provides an extensive repertoire of tools to analyse key properties. The paper describes how routing techniques can be modelled as games and presents some analytical results.     

一种异构多接口多信道无线网络的信道分配算法

多接口多信道技术是无线网络环境中减少链路干扰、提高网络吞吐量的有效途径,但如何合理有效地进行信道分配已成为多接口多信道无线网络所面临的主要问题之一.针对自私的网络节点,本文使用非合作博弈对异构条件下多接口节点的信道分配问题进行建模分析,其纳什均衡解为解决该问题所需的稳定的信道分配方案.本文首先讨论纳什均衡的存在条件并提出实现纳什均衡的分布式算法.此外,考虑到实际网络中节点仅能感知局部信道信息以及接口工作信道受限等因素,本文进一步改进算法并通过仿真实验对其收敛性进行证明.     

基于博弈论的信息安全技术评价模型

信息安全在企业信息系统建设中越来越重要,如何评价信息安全技术成为当前的一个研究课题.文中基于博弈论,对由防火墙、入侵检测系统和容忍入侵技术构成的三层安全体系结构进行了分析,提出了对信息安全技术进行评价的模型.在对入侵检测系统分析评价的基础上,重点分析了防火墙、入侵检测与容忍入侵的相互影响和关系.研究表明,IDS的检测率、误报率与防火墙的性能有密切关系,系统安全配置直接影响信息安全机制的性能和成本效益,容忍入侵机制取决于入侵的损失评估、系统的成本和防火墙与IDS的性能.信息安全机制的优化配置对于信息安全的效果具有重要影响.     

A Game Theoretic Approach to the Analysis of Dynamic Networks

A model of dynamic networks is introduced which incorporates three kinds of network changes: deletion of nodes (by faults or sabotage), restoration of nodes (by actions of “repair”), and creation of nodes (by actions that extend the network). The antagonism between the operations of deletion and restoration resp. creation is modelled by a game between the two agents “Destructor” and “Constructor”. In this framework of dynamic model-checking, we consider as specifications (“winning conditions” for Constructor) elementary requirements on connectivity of those networks which are reachable from some initial given network. We show some basic results on the (un-)decidability and hardness of dynamic model-checking problems.     

Estimating a Game Theoretic Model

Applied game theory generally shows possible game situations, discussingtheir implications. This paper derives an estimation procedure fornon-cooperative games, using cross-section data. During thetransformation of the data an attempt is made to pick extreme points, tofind the outer borders for the actual game. The estimation procedure isan algorithm that switches between several software packages. In anapplication a motivated choice can be made for the transformationfunctions such that the estimated game comes closest to the actual gameof the empirical problem under study. To demonstrate the estimationprocedure, a situation is studied in which a villager voluntarilychooses a level of participation in forest management.     

Computational nuclear quantum many-body problem: The UNEDF project

The UNEDF project was a large-scale collaborative effort that applied high-performance computing to the nuclear quantum many-body problem. The primary focus of the project was on constructing, validating, and applying an optimized nuclear energy density functional, which entailed a wide range of pioneering developments in microscopic nuclear structure and reactions, algorithms, high-performance computing, and uncertainty quantification. UNEDF demonstrated that close associations among nuclear physicists, mathematicians, and computer scientists can lead to novel physics outcomes built on algorithmic innovations and computational developments. This review showcases a wide range of UNEDF science results to illustrate this interplay.     

一种低计算量移动机器人自定位方法

移动机器人定位问题就是通过传感器数据来确定自己的位姿。本文介绍了几种基于概率的自定位算法。针对蒙特卡罗定位算法需要精确概率模型以及计算量大的问题,本文提出了一种均匀蒙特卡罗算法。该算法假设运动模型和感知模型都是均匀分布的,采样点在运动过程中不变,而且不需要精确的概率模型,计算量小,稳定性高。试验表朗,该算法能在室内环境下很好的对机器人定位。     

基于交互式自适应概率数据关联的目标跟踪算法

在联合交互式多模型概率数据关联思想的基础上,将自适应滤波算法应用到概率数据关联滤波器中,提出了一种适用于杂波环境机动目标跟踪的新算法-交互式自适应概率数据关联(Interactive Multiple Models Adaptive Probabilistic Data Association-IMM-APDA)算法,避免了模型选取的不确定性,扩大了机动目标的跟踪范围,实现了杂波环境中对目标较高精度的状态估计.理论分析与仿真结果验证了该算法的优越性,提高了目标跟踪精度.     

线性离散时变系统的状态和输入混合估计: 一种对策方法

本文研究了线性离散时变系统的混合估计问题, 估计信号是状态和输入的线性组合. 设计目标要求满足一个最坏性能指标, 即从扰动到估计误差的能量增益小于一个给定值. 混合估计问题的最优解是二人零和微分对策的鞍点解. 基于微分对策方法, 混合估计问题有解的充要条件表达为 Riccati 微分方程的可解性. 在问题有解时, 给出了符合要求的估计器. 估计器的结构表达为一个增益矩阵和一个输出映射矩阵, 后者反映了未知输入与输出估计误差之间的内在联系. 最后, 通过数值例子证明了本文方法的有效性.     

线性连续时变系统的状态和输入混合估计:一种对策方法

The $H_\infty$ hybrid estimation problem for linear continuous time-varying systems is investigated in this paper, where estimated signals are linear combination of state and input. Design objective requires the worst-case energy gain from disturbance to estimation error be less than a prescribed level. Optimal solution of the hybrid estimation problem is the saddle point of a two-player zero sum differential game. Based on the differential game approach, necessary and sufficient solvable conditions for the hybrid estimation problem are provided in\hfill terms\hfill of\hfill solutions\hfill to\hfill a\hfill Riccati\hfill diffe-\\rential equation. Moreover, one possible estimator is proposed if the solvable conditions are satisfied. The estimator is characterized by a gain matrix and an output mapping matrix that reflects the internal relations between the unknown input and output estimation error. Both state and unknown inputs estimation are realized by the proposed estimator. Thus, the results in this paper are also capable of dealing with fault diagnosis problems of linear time-varying systems. At last, a numerical example is provided to illustrate the proposed approach.     

Hybrid Estimation of State and Input for Linear Continuous Time-varying Systems:A Game Theory Approach

1IntroductionWhen estimated signal includes both state and unknown input of the system,estimation problemis referred to as state and input hybrid estimation(in the following,only hybrid estimation will beused for brevity).Hybrid estimation is originated f…     

Game Theoretic subcarrier and power allocation for wireless OFDMA networks

This paper proposes a bargaining game theoretic resource(including the subcarrier and the power) allocation scheme for wireless orthogonal frequency division multiple access(OFDMA) networks.We define a wireless user s payoff as a function of the achieved data-rate.The fairness resource allocation problem can then be modeled as a cooperative bargaining game.The objective of the game is to maximize the aggregate payoffs for the users.To search for the Nash bargaining solution(NBS) of the game,a suboptimal subcarrier allocation is performed by assuming an equal power allocation.Thereafter,an optimal power allocation is performed to maximize the sum payoff for the users.By comparing with the max-rate and the max-min algorithms,simulation results show that the proposed game could achieve a good tradeoff between the user fairness and the overall system performance.     

Rough Sets and Multivariate Statistical Classification: A Simulation Study

The classification of a set of objects into predefined homogenous groups is a problem with major practical interest in many fields. Over the past two decades several non-parametric approaches have been developed to address the classification problem, originating from several scientific fields. This paper is focused on the rough sets approach and the investigation of its performance as opposed to traditional multivariate statistical classification procedures, namely the linear discriminant analysis, the quadratic discriminant analysis and the logit analysis. For this purpose an extensive Monte Carlo simulation is conducted to examine the performance of these methods under different data conditions.     

A multiagent dynamic interaction testbed: Theoretic framework, system architecture and experimentation

Recent research on Distributed Artificial Intelligence(DAI)has focused upon agents‘ interaction in Multiagent System.sThis paper presents a text understanding oriented multiagent dynamic interaction testbed(TUMIT);the theoretic framework based upon game theory,the free-market-like system marchitecture,and experimentation on TUMIT.Unlike other DAI testbeds,TUMIT views different text understanding(TU)methods as different“computational resources”,and makes agents choose different TU paths and computational resources according to the resouce information on the bulletins in their hostcomputer.Therefore,in TUMIT,task allocation is wholly distributed.This makes TUMIT work like a “free market”.In such a system,agents‘choices and resource load may oscillate.It is shown theoretically and experimentally that if agents use multi-level of “history information”,their behavior will tend to converge to a Nash equilibrium situation;and that if agents use “fecall-forget” strategy on “history information”,the convergence can be accelerated and the agents can acclimate themselves to changed environment.Compared with other DAI testbeds,TUMIT is more distributed,and the agents in TUMIT are more adaptive to the dynamic environment.     

Game Theory and Decision Theory in Multi-Agent Systems

In the last few years, there has been increasing interest from the agent community in the use of techniques from decision theory and game theory. Our aims in this article are firstly to briefly summarize the key concepts of decision theory and game theory, secondly to discuss how these tools are being applied in agent systems research, and finally to introduce this special issue of Autonomous Agents and Multi-Agent Systems by reviewing the papers that appear.     

Game theory approach to state and input simultaneous estimation for discrete-time LTV systems

This article aims to provide a simple approach realising state observation and input estimation simultaneously for discrete-time LTV systems in H_∞ setting. Through solving a two-player zero sum differential game, appealing results are obtained in two folds. First, necessary and sufficient solvability conditions for state and input simultaneous estimation problem are given in terms of solution to a set of difference Riccati recursion. Second, one estimator is presented with special innovation structure, where innovation information is used to update state observation tuned by gain matrix and to provide input estimation through a projector matrix, where gain matrix and projector matrix are constructed from solution to difference Riccati recursion. At last, simulation results are provided to justify proposed approach.     

教育游戏理论与开发

教育游戏是指将学习因素和电脑游戏因素有机结合在一起的交互式活动系统,它融知识性和娱乐性于一体,真正实现寓教于乐。教育游戏建立在建构主义、沉浸理论和多元智能等理论基础之上,文章对教育游戏开发应当遵循的原则也进行了详细探讨。