基于Multi-Agent的航空集群系统重构机理研究

集群作战是未来空中作战的主要样式,其重构机理的研究对于提升航空集群执行任务的整体性能以及在遭受攻击时的生存能力具有重要作用。从作战任务层面出发,对集群系统重构的定义以及类型、触发机制和基本原则进行了分析阐述;借鉴博弈论理论,将航空集群系统分布式重构问题映射为多Agent之间的合作-竞争问题,建立了基于Multi-Agent的系统重构模型,给出了重构的流程与算法,为航空集群系统自适应重构能力的设计实现提供了理论技术支持。     

More on the search for an infiltrator

We have asymptotically solved a discrete search game on an array of n ordered cells with two players: infiltrator (hider) and searcher, when the probability of survival approaches 1. The infiltrator wishes to reach the last cell in finite time, and the searcher has to defend that cell. When the players occupy the same cell, the searcher captures the infiltrator with probability 1 ? z. The payoff to the hider is the probability that the hider reaches the last cell without getting captured. © 2002 John Wiley & Sons, Inc. Naval Research Logistics, 49: 1–14, 2002; DOI 10.1002/nav.1047     

军用认知无线电网络中功率控制博弈算法的探讨

通过对比军用认知无线功率控制博弈模型与一些经典的功率控制博弈算法,提出了一种认知无线电网络中的功率控制的博弈算法。通过仿真结果性能分析,证明算法具有一定的优越性,并且能够为军事无线通信提供一定的理论依据和现实作用。     

地方物流供应商“偷懒”行为监管机制研究

军民融合现代军事物流体系中,地方物流供应商往往会"偷懒",这必将损害军民融合现代军事物流体系的整体绩效。首先描述了"偷懒"行为及其危害性,从机制入手,基于博弈论建立地方物流供应商"偷懒"行为监管机制;分析并证明了监管机制的有效性。这一机制的构建对于监管地方物流供应商具有重要作用。     

计数序贯抽样检验方案的OC函数和ASN

根据序贯概率比检验的理论,给出计数序贯抽样检验方案的设计原理,得到国家标准GB／T8051—2008中计数序贯抽样检验方案的OC函数和ASN的计算方法。     

搜索路径给定时的搜索对策问题及其应用

搜索路径给定时的最优搜索方案问题,也可以理解为是关于搜索者和目标的二人对策问题,主要讨论了当搜索路径给定时的单个搜索者和单个目标的搜索对策问题。首先根据问题的特点,利用动态规划和迭代的方法,确定关于目标逃逸路径混合策略的最优分区,证明该分区是多面体凸集;针对目标不同逃逸路径的分区,求出搜索者的最大期望收益,再将问题转化为二人有限零和对策,计算出搜索者的支付矩阵,确定最优搜索策略。最后结合海军护航行动,对我舰载直升机搜索小型海盗船进行分析和计算,说明搜索路径给定时的最优搜索对策对于双方的资源分配和提高搜索效率具有一定的应用价值。     

网络游戏赌博性质分析

网络游戏产业快速发展的同时,赌博现象混迹其中。网络游戏赌博与传统赌博只是形式上的不同,其实质就是一种赌博行为。只有从教育、法制、监督、自律四方面综合治理,才能使这个行业健康发展。     

COOPERATION,SIGNALS, AND SANCTIONS

This paper uses game theory and modeling to address the role of incentive structures and information dynamics in nuclear inspections. The traditional argument is that compliant states should be willing to allow inspections to prove their innocence, while proliferating states are likely to impede inspections. This argument does not take into account the historical variation in inspection, signaling, and sanctioning behaviors. Using a game theoretic analysis and model, it is shown that the separation of proliferators from nonproliferators only occurs when the likelihood of proliferation is high and punishment costs are moderate. The model assumes that states can choose how much to cooperate with inspectors and must pay opportunity or secrecy costs when inspections are effective. The results are tested against a set of real-life cases, providing support for the claims of historical variation and the model's deductive propositions.     

Limiting behavior of the stochastic sequential assignment problem

The stochastic sequential assignment problem (SSAP) considers how to allocate available distinct workers to sequentially arriving tasks with stochastic parameters such that the expected total reward obtained from the sequential assignments is maximized. Implementing the optimal assignment policy for the SSAP involves calculating a new set of breakpoints upon the arrival of each task (i.e., for every time period), which is impractical for large‐scale problems. This article studies two problems that are concerned with obtaining stationary policies, which achieve the optimal expected reward per task as the number of tasks approaches infinity. The first problem considers independent and identically distributed (IID) tasks with a known distribution function, whereas in the second problem tasks are derived from r different unobservable distributions governed by an ergodic Markov chain. The convergence rate of the expected reward per task to the optimal value is also obtained for both problems. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

A three‐stage procurement optimization problem under uncertainty

This article examines a problem faced by a firm procuring a material input or good from a set of suppliers. The cost to procure the material from any given supplier is concave in the amount ordered from the supplier, up to a supplier‐specific capacity limit. This NP‐hard problem is further complicated by the observation that capacities are often uncertain in practice, due for instance to production shortages at the suppliers, or competition from other firms. We accommodate this uncertainty in a worst‐case (robust) fashion by modeling an adversarial entity (which we call the “follower”) with a limited procurement budget. The follower reduces supplier capacity to maximize the minimum cost required for our firm to procure its required goods. To guard against uncertainty, the firm can “protect” any supplier at a cost (e.g., by signing a contract with the supplier that guarantees supply availability, or investing in machine upgrades that guarantee the supplier's ability to produce goods at a desired level), ensuring that the anticipated capacity of that supplier will indeed be available. The problem we consider is thus a three‐stage game in which the firm first chooses which suppliers' capacities to protect, the follower acts next to reduce capacity from unprotected suppliers, and the firm then satisfies its demand using the remaining capacity. We formulate a three‐stage mixed‐integer program that is well‐suited to decomposition techniques and develop an effective cutting‐plane algorithm for its solution. The corresponding algorithmic approach solves a sequence of scaled and relaxed problem instances, which enables solving problems having much larger data values when compared to standard techniques. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013