Mastering air combat game with deep reinforcement learning

Reinforcement learning has been applied to air combat problems in recent years,and the idea of curriculum learning is often used for reinforcement learning,but traditional curriculum learning suffers from the problem of plasticity loss in neural networks.Plasticity loss is the difficulty of learning new knowledge after the network has converged.To this end,we propose a motivational curriculum learning distributed proximal policy optimization(MCLDPPO)algorithm,through which trained agents can significantly outperform the predictive game tree and mainstream reinforcement learning methods.The motivational curriculum learning is designed to help the agent gradually improve its combat ability by observing the agent's unsatisfactory performance and providing appropriate rewards as a guide.Furthermore,a complete tactical maneuver is encapsulated based on the existing air combat knowledge,and through the flexible use of these maneuvers,some tactics beyond human knowledge can be realized.In addition,we designed an interruption mechanism for the agent to increase the frequency of decisionmaking when the agent faces an emergency.When the number of threats received by the agent changes,the current action is interrupted in order to reacquire observations and make decisions again.Using the interruption mechanism can significantly improve the performance of the agent.To simulate actual air combat better,we use digital twin technology to simulate real air battles and propose a parallel battlefield mechanism that can run multiple simulation environments simultaneously,effectively improving data throughput.The experimental results demonstrate that the agent can fully utilize the situational information to make reasonable decisions and provide tactical adaptation in the air combat,verifying the effectiveness of the algorithmic framework proposed in this paper.

A function-based behavioral modeling method for air combat simulation

Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics.Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel,execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.

Optimal confrontation position selecting games model and its application to one-on-one air combat

In the air combat process,confrontation position is the critical factor to determine the confrontation situation,attack effect and escape probability of UAVs.Therefore,selecting the optimal confrontation position becomes the primary goal of maneuver decision-making.By taking the position as the UAV’s maneuver strategy,this paper constructs the optimal confrontation position selecting games(OCPSGs)model.In the OCPSGs model,the payoff function of each UAV is defined by the difference between the comprehensive advantages of both sides,and the strategy space of each UAV at every step is defined by its accessible space determined by the maneuverability.Then we design the limit approximation of mixed strategy Nash equilibrium(LAMSNQ)algorithm,which provides a method to determine the optimal probability distribution of positions in the strategy space.In the simulation phase,we assume the motions on three directions are independent and the strategy space is a cuboid to simplify the model.Several simulations are performed to verify the feasibility,effectiveness and stability of the algorithm.

Mission-oriented capability evaluation for combat network based on operation loops

With continuous growth in scale,topology complexity,mission phases,and mission diversity,challenges have been placed for efficient capability evaluation of modern combat systems.Aiming at the problems of insufficient mission consideration and single evaluation dimension in the existing evaluation approaches,this study proposes a mission-oriented capability evaluation method for combat systems based on operation loop.Firstly,a combat network model is given that takes into account the capability properties of combat nodes.Then,based on the transition matrix between combat nodes,an efficient algorithm for operation loop identification is proposed based on the Breadth-First Search.Given the mission-capability satisfaction of nodes,the effectiveness evaluation indexes for operation loops and combat network are proposed,followed by node importance measure.Through a case study of the combat scenario involving space-based support against surface ships under different strategies,the effectiveness of the proposed method is verified.The results indicated that the ROI-priority attack method has a notable impact on reducing the overall efficiency of the network,whereas the O-L betweenness-priority attack is more effective in obstructing the successful execution of enemy attack missions.

Tactical reward shaping for large-scale combat by multi-agent reinforcement learning

Future unmanned battles desperately require intelli-gent combat policies,and multi-agent reinforcement learning offers a promising solution.However,due to the complexity of combat operations and large size of the combat group,this task suffers from credit assignment problem more than other rein-forcement learning tasks.This study uses reward shaping to relieve the credit assignment problem and improve policy train-ing for the new generation of large-scale unmanned combat operations.We first prove that multiple reward shaping func-tions would not change the Nash Equilibrium in stochastic games,providing theoretical support for their use.According to the characteristics of combat operations,we propose tactical reward shaping(TRS)that comprises maneuver shaping advice and threat assessment-based attack shaping advice.Then,we investigate the effects of different types and combinations of shaping advice on combat policies through experiments.The results show that TRS improves both the efficiency and attack accuracy of combat policies,with the combination of maneuver reward shaping advice and ally-focused attack shaping advice achieving the best performance compared with that of the base-line strategy.

Combating Cholera

Lying in her makeshift hospital bed,Joyce Tembo thanked medical personnel for evacuating her to the designated national cholera treatment centre,6 km north of Zambia’s capital Lusaka.She was recently diagnosed with diarrhoeal disease.Tembo,43,commended the medical sta!stationed at the treatment centre for their great service to thousands of patients,especially women and children seeking urgent treatment.“I am very grateful to the Chinese doctors who attended to me as soon as the ambulance rushed me to the clinic where I received urgent treatment;they have really saved my life,”Tembo told ChinAfrica.But not all residents in her community are as lucky as her.Many in the densely populated slums die every day due to the area’s poor sanitation-one of the major causes of the cholera outbreak.

Strategies for Core Strength Training in Free Combat Teaching

Since free combat is a competitive sport that flexibly utilizes kicking,punching,wrestling,and holding techniques to defeat the opponent,a good core strength of athletes can help to improve the technical level,enhance the quality of movements,and protect the joints and muscles.In order to carry out core strength training in free combat teaching with high quality,firstly,it is necessary for coaches to carry out simple training,centralized training,and extended training according to the basic planning of adaptation-stabilization-improvement.Secondly,it is also important to test the athlete’s physical and athletic qualities before implementing the specific training plan,optimize the training program,and carry out statistical analysis of the stage training data in order to achieve the best training effect.

“情指行”一体化实战运行机制完善路径

随着社会治安治理工作的顺利推进,各地公安机关把大数据智能化建设作为科技兴警的重要抓手,依托本区域警情特点,深入推进“情指行”一体化实战运行机制改革,积极创新,延伸拓展,取得一定成效。但由于缺乏系统理念,各区域标准不一,导致各地发展进程不一致,工作机制还不够完善。因此,推进“情指行”一体化实战运行机制改革,重视研究开发预警系统,及时、有效地预防和处置各类警情,是响应和推进“建立完善‘专业+机制+大数据’新型警务运行模式,不断优化警务管理体制、运行机制,大力推进公安大数据智能化建设应用”的重要内容。研究构建以监测子系统、信息处理子系统、分析子系统、警报子系统及指令子系统为支撑的预警模型,并以人口变量为例深入分析预警模型的策略集合,以期对影响社会治安防控的各类信息进行深度发掘,发现数据背后的隐形规律并进行风险研判,实现对涉赌、涉黄、涉稳等人员的预测预判,针对不同风险级别发出预警指令,提升公安机关的风险防控能力。

HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA

Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.

公安院校新型警务人才培养赋能公安机关新质战斗力研究——以广东警官学院为例

公安院校是培养现代化新型警务人才的基地,为形成和提升公安机关新质战斗力提供高质量教育力量。围绕“专业+机制+大数据”新型警务人才培养模式,进一步强化公安院校政治能力,增强办学水平,锻造过硬队伍,深化学校内涵式建设,是当前公安院校构建现代化教育改革体系的重要课题。因此,要持续聚焦建立完善“专业+机制+大数据”新型警务运行模式,优化警务管理体制、运行机制,大力推进公安大数据智能化建设应用,加快形成和提升公安机关新质战斗力,积极推进公安工作现代化,实现公安教育工作整体提档升级。

ASSESSMENT OF AIRCRAFT COMBAT SURVIVABILITY ENHANCED BY COMBINED RADAR STEALTH AND ONBOARD ELECTRONIC ATTACK

The combat survivability is an essential factor to be considered in the development of recent military aircraft. Radar stealth and onboard electronic attack are two major techniques for the reduction of aircraft susceptibility. A tactical scenario for a strike mission is presented. The effect of aircraft radar cross section on the detection probability of a threat radar, as well as that of onboard jammer, are investigated. The guidance errors of radar guided surface to air missile and anti aircraft artillery, which are disturbed by radar cross section reduction or jammer radiated power and both of them are determined. The probability of aircraft kill given a single shot is calculated and finally the sortie survivability of an attack aircraft in a supposed hostile thread environment worked out. It is demonstrated that the survivability of a combat aircraft will be greatly enhanced by the combined radar stealth and onboard electronic attack, and the evaluation metho dology is effective and applicable.

OPTIMIZATION FOR COMBAT CONFIGURATION OF AIR DEFENSE WEAPON SYSTEMS

At evaluating the combat effectiveness of the defense system, target′s probability to penetrate the defended area is a primary care taking index. In this paper, stochastic model to compete the probability that target penetrates the defended area along any flight path is established by the state analysis and statistical equilibrium analysis of stochastic service system theory. The simulated annealing algorithm is an enlightening random search method based on Monte Carlo recursion, and it can find global optimal solution by simulating annealing process. Combining stochastic model to compete the probability and simulated annealing algorithm, this paper establishes the method to solve problem quantitatively about combat configuration optimization of weapon systems. The calculated result shows that the perfect configuration for fire cells of the weapon is fast found by using this method, and this quantificational method for combat configuration is faster and more scientific than previous one based on principle via map fire field.

公安机关新质战斗力的内涵及提升路径

政治建警是提升公安机关新质战斗力的根本保障,必须以习近平新时代中国特色社会主义思想作为行动指南;科技兴警是提升公安机关新质战斗力的核心要素,必须摆在更加突出的位置聚焦聚力推进;改革强警是提升公安机关新质战斗力的重要动力,必须依靠体制机制创新打通堵点卡点;素质育警是提升公安机关新质战斗力的第一资源,必须重点培育符合公安机关新质战斗力发展需求的专业人才。

不确定态势下无人机群协同作战效能评估

针对不确定态势下无人机群作战效能评估问题,构建了一套基于作战过程仿真的无人机群作战效能评估方法。首先,从作战环角度分析无人机群察打任务作战过程;其次,根据作战环的各个步骤设计战法计算,结合设计的参数设置、战场态势生成与记录、全局进度控制器、作战数据记录等模块形成完整的无人机群察打作战过程仿真系统;然后,依据获得的作战过程仿真结果数据,构建核心效能指标与辅助效能指标,给出各指标的计算与耦合方法;最后,通过案例实验,对采用不同编配方案的无人机群进行作战过程仿真,评估分析作战效能,验证了本文方法的可行性,可实现不确定态势下无人机群作战效能的有效评估。

基于最优探测航线椭圆拟合的战斗巡逻规划方法

针对远海弱情报支援背景下的空中编队战斗巡逻规划问题,提出了一种新的基于最优探测航线椭圆拟合的巡逻空域规划方法。以协同概率探测面积作为遗传算法的适应度函数,求解最优飞机协同探测航线,根据初始航路点列的统计学特征,提出了基于几何距离的双点移除椭圆拟合方法,求解航空兵实际可用的椭圆航线及对应巡逻空域。仿真结果表明,编队按照规划的矩形巡逻空域和椭圆航线进行战斗巡逻,可保持对总任务区域82.16%的协同探测,与最优雷达探测航线的平均实时探测范围相比仅下降1.93%,且探测盲区时间空窗小。规划结果能显著降低飞行员的认知载荷,具有较好的实用价值和现实意义。

“战斗力”能译成“combativeness”吗?

新华网英文版在一篇报道中把军事术语"战斗力"译成了"combativeness"。通过分析十四部英汉词典中"combativeness"的释义、八部汉英词典给出的"战斗力"的译法以及基于问卷调查的结果,作者指出了新华网这一译法的错误,补充了汉英词典未收录的"战斗力"的两种译法,并强调加强外宣翻译中政治意识的重要性。

基于作战效果的舰炮作战效能分析模型

针对舰炮系统基于作战效果进行效能分析的问题,通过ADC法和层次分析法建立效能分析模型。根据作战任务,分别给出了可用度向量、可信度矩阵和能力向量的评估模型。在能力向量的评估模型中提出作战任务与打击效果、任务时间和弹药消耗有关,结合层次分析法给出了具体的计算模型,为指挥员做出决策提供了有力支撑。针对具体的计算模型,给出了示例,通过计算比较,研究人员可选择最佳执行方案,同时可根据实际情况对参数权重进行重新计算,从而提供更加合理准确的决策结果。

如何加强新形势下消防救援队伍战勤保障体系建设

立足新形势下,消防救援队伍职能与责任进一步拓宽,所面临的救援需求、救援环境、救援对象等呈现为多元化趋势,为了应对新问题、新挑战、新要求,进一步加强消防救援队伍战勤保障体系建设是应有之义。基于差异化层面解读消防救援队伍战勤保障体系的必要性,围绕“衣食住行医”等多个方面分析保障体系面临的问题,进而从器材装备、社会联勤、立体运输、模块存储及人才队伍5个方面,提出消防救援队伍战勤保障体系建设的有效性建议,旨在促进消防救援工作的职业化、专业化、系统化,为维护社会繁荣稳定作出积极贡献。

新质生产力与新质战斗力高效融合、双向互促的机制、框架与进路--量子信息技术的视角

以量子信息技术在经济社会及军事领域的应用为切入点,探讨新质生产力与新质战斗力在量子信息技术赋能下高效融合、双向互促的逻辑机制、理论框架与实践进路。研究新质生产力与新质战斗力在量子信息技术加持下的特点与趋势,揭示二者之间的内在联系、互动规律和相互作用机制,从系统理论、协同理论和军民融合理论的角度出发,构建二者高效融合、双向互促的三重循环理论框架,给出实现这一目标的策略路径。研究表明,量子信息技术的突破为新质生产力和新质战斗力的提升提供了强大动力,新质生产力通过颠覆性技术突破和产业升级赋能新质战斗力的提升,同时新质战斗力又以军事需求和产业应用反作用于新质生产力,形成新质生产力和新质战斗力的高效融合、双向互促的局面,为经济社会发展和军事战略转型提供新的思路与方向。基于以上研究结论,建议通过强化技术创新、推动产业升级、优化资源配置等举措,促进新质生产力和新质战斗力的高效融合、双向互促。

Situation assessment for air combat based on novel semi-supervised naive Bayes

A method is proposed to resolve the typical problem of air combat situation assessment. Taking the one-to-one air combat as an example and on the basis of air combat data recorded by the air combat maneuvering instrument, the problem of air combat situation assessment is equivalent to the situation classification problem of air combat data. The fuzzy C-means clustering algorithm is proposed to cluster the selected air combat sample data and the situation classification of the data is determined by the data correlation analysis in combination with the clustering results and the pilots＇ description of the air combat process. On the basis of semi-supervised naive Bayes classifier, an improved algorithm is proposed based on data classification confidence, through which the situation classification of air combat data is carried out. The simulation results show that the improved algorithm can assess the air combat situation effectively and the improvement of the algorithm can promote the classification performance without significantly affecting the efficiency of the classifier.