A search game taking account of attributes of searching resources

This article deals with a two‐person zero‐sum game called a search allocation game (SAG), in which a searcher and a target participate as players. The searcher distributes his searching resources in a search space to detect the target. The effect of resources lasts a certain period of time and extends to some areas at a distance from the resources' dropped points. On the other hand, the target moves around in the search space to evade the searcher. In the history of search games, there has been little research covering the durability and reachability of searching resources. This article proposes two linear programming formulations to solve the SAG with durable and reachable resources, and at the same time provide an optimal strategy of distributing searching resources for the searcher and an optimal moving strategy for the target. Using examples, we will analyze the influences of two attributes of resources on optimal strategies. © 2007 Wiley Periodicals, Inc. Naval Research Logistics 2008     

An attacker‐defender model for analyzing the vulnerability of initial attack in wildfire suppression

Wildfire managers use initial attack (IA) to control wildfires before they grow large and become difficult to suppress. Although the majority of wildfire incidents are contained by IA, the small percentage of fires that escape IA causes most of the damage. Therefore, planning a successful IA is very important. In this article, we study the vulnerability of IA in wildfire suppression using an attacker‐defender Stackelberg model. The attacker's objective is to coordinate the simultaneous ignition of fires at various points in a landscape to maximize the number of fires that cannot be contained by IA. The defender's objective is to optimally dispatch suppression resources from multiple fire stations located across the landscape to minimize the number of wildfires not contained by IA. We use a decomposition algorithm to solve the model and apply the model on a test case landscape. We also investigate the impact of delay in the response, the fire growth rate, the amount of suppression resources, and the locations of fire stations on the success of IA.     

信息化条件下基于大数据的后装一体化保障构想

由于契约的不完全性、专用性资产的存在,导致民企参军的谈判过程中,一方可能利用另一方因专用性资产投资的锁定效应,而采取机会主义行为将另一方套牢,攫取可占用专用性准租金。这种套牢风险不是单向的,而是双向的,双向套牢风险的存在不仅会降低军品科研生产项目建设质量,而且还会严重挫伤民营企业承担军品科研生产任务的积极性。为有效防范双向套牢风险,本文将从博弈论的角度,建立民营企业与军队采办部门的期望收益与专用性资产投资的函数关系和博弈矩阵,分阶段讨论民营企业与军队采办部门所面临的套牢风险大小及各自的最佳行为选择。以降低民企参军面临的套牢风险,引导更多优势民营企业进入军品科研生产与维修领域,推动军民融合的深度发展。     

光顺样条函数的再生核表示与计算

光顺样条是散乱数据拟合的理想函数,是噪声数据最优平滑的重要工具。因此,光顺样条的数学表示和计算的研究具有重要的意义。本文在一般的线性微分算子和线性泛函的情况下讨论光顺样条函数的构造和计算,通过构造一个适当的再生核Hilbert空间,使得所讨论的微分算子光顺样条成为该空间中的最小范数问题,再利用投影理论建立了光顺样条函数的再生核表示方法,并得到了插值偏差表达式。作为特例,还给出了奇次多项式光顺样条函数新的简洁的计算方法。     

一个组合对策问题的解

本文求解如下的组合对策问题:设有一堆棋子,总数N 是奇数,甲乙两人轮流取子,每人每次可取一颗、二颗,最多可取s 颗,但不能不取,直至取完后分别来数甲乙两人所取棋子的总数,总数为奇数者获胜。站在甲的立场上考虑获胜的策略,文中解决了如下两个问题:(Ⅰ)总数N 应是什么样的奇数,甲才有获胜策略;(Ⅱ)当N 一定时,甲应采取什么样的策略取子,才能获胜。     

一阶非线性脉冲边值问题解的存在性

研究一阶非线性脉冲周期边值问题，应用微分不等式和Schaefer不动点定理，得到了脉冲边值问题解存在的充分性判据，并给出了相应的Green函数。     

并联机构中奇异性的稳定性问题

将映射理论中的临界点稳定性概念引入并联机构的奇异性分析中 ,提出稳定奇异性和非稳定奇异性的概念 ,研究了低维并联机构奇异性的稳定性情况 ,对几种典型机构的分析验证了这种分类的合理性。提出的分析方法可以为并联机构的机构设计和控制等方面提供理论依据。     

集群搜索对策理论在搜索-规避对抗中的应用

使用对策论的观点和方法 ,结合搜索论的知识 ,建立了一类搜索 -规避对抗对策模型 .对模型的结论做了系统分析 ,考虑了对策双方的最优策略及使用 .     

含非线性半群的微分包含

研究了形如X＇（t）∈-Ax（t）＋F（t,x（t））,0≤t≤T,X（0）＝x0的微分包含解的存在的局部性和整体性的结果,并在某种条件下研究了解的稳定性。     

Technical note: Find a hidden “treasure”

This paper deals with a two searchers game and it investigates the problem of how the possibility of finding a hidden object simultaneously by players influences their behavior. Namely, we consider the following two‐sided allocation non‐zero‐sum game on an integer interval [1,n]. Two teams (Player 1 and 2) want to find an immobile object (say, a treasure) hidden at one of n points. Each point i ∈ [1,n] is characterized by a detection parameter λ_i (μ_i) for Player 1 (Player 2) such that p_i(1 ? exp(?λ_ix_i)) (p_i(1 ? exp(?μ_iy_i))) is the probability that Player 1 (Player 2) discovers the hidden object with amount of search effort x_i (y_i) applied at point i where p_i ∈ (0,1) is the probability that the object is hidden at point i. Player 1 (Player 2) undertakes the search by allocating the total amount of effort X(Y). The payoff for Player 1 (Player 2) is 1 if he detects the object but his opponent does not. If both players detect the object they can share it proportionally and even can pay some share to an umpire who takes care that the players do not cheat each other, namely Player 1 gets q₁ and Player 2 gets q₂ where q₁ + q₂ ≤ 1. The Nash equilibrium of this game is found and numerical examples are given. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007