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.

UAV cooperative air combat maneuver decision based on multi-agent reinforcement learning

In order to improve the autonomous ability of unmanned aerial vehicles(UAV)to implement air combat mission,many artificial intelligence-based autonomous air combat maneuver decision-making studies have been carried out,but these studies are often aimed at individual decision-making in 1 v1 scenarios which rarely happen in actual air combat.Based on the research of the 1 v1 autonomous air combat maneuver decision,this paper builds a multi-UAV cooperative air combat maneuver decision model based on multi-agent reinforcement learning.Firstly,a bidirectional recurrent neural network(BRNN)is used to achieve communication between UAV individuals,and the multi-UAV cooperative air combat maneuver decision model under the actor-critic architecture is established.Secondly,through combining with target allocation and air combat situation assessment,the tactical goal of the formation is merged with the reinforcement learning goal of every UAV,and a cooperative tactical maneuver policy is generated.The simulation results prove that the multi-UAV cooperative air combat maneuver decision model established in this paper can obtain the cooperative maneuver policy through reinforcement learning,the cooperative maneuver policy can guide UAVs to obtain the overall situational advantage and defeat the opponents under tactical cooperation.

On intelligent Cooperative System

The paper presents our research efforts motivated by the apparent need to combine conventional,preexisting computing functions with novel knowledge--based functions. This has been likened to what occurred in the evolution of primates, where the 'new brain' (the cortex) was added to, layered upon, and given control over the 'old brain' common to the less complex animals.

Straight-Going Priority in Hierarchical Control Framework for Right-Turning Vehicle Merging Based on Cooperative Game

With the development of connected and automated vehicles(CAVs),forming strategies could extend from the typically used first-come-first-served rules.It is necessary to consider passing priorities when crossing intersections to prevent conflicts.In this study,a hierarchical strategy based on a cooperative game was developed to improve safety and efficiency during right-turning merging.A right-turn merging conflict model was established to analyze the right-turning vehicle characteristics of the traffic flow.The proposed three-layered hierarchical strategy includes a decision-making layer,a task layer,and an operation layer.A decision-making-layer cooperative game strategy was used to determine the merging priority of straight-going traffic and right-turning flows.In addition,a task-layer cooperative game strategy was designed for the merging sequence.A modified consensus algorithm was utilized to optimize the speed of vehicles in the virtual platoon of the operation layer.Traffic simulations were performed on the PYTHON-SUMO integrated platform to verify the proposed strategy.The simulation results show that,compared with other methods,the proposed hierarchical strategy has the shortest travel time and loss time and performs better than other methods when the straight-going traffic flow increases during right-turning merging at the intersection.The proposed method shows superiority under a significant traffic flow with a threshold of 900 vehicle/(h·lane).This satisfactory application of right-turning merging might be extended to ramps,lane-changing,and other scenarios in the future.

Design method of organizational structure for MAVs and UAVs heterogeneous team with adjustable autonomy

The increasingly complex battlefield environment requests much closer connection in a team having both manned and unmanned aerial vehicles(MAVs and UAVs). This special heterogeneous team structure causes demands for effective organizational structure design solutions. Implementing adjustable autonomy in the organizational structure, the expected evaluation function is established based on the physical resource, intelligent resource, network efficiency, network vulnerability and task execution reliability. According to the above constraints, together with interaction latency, decision-making information processing capacity, and decision-making latency, we aim to find a preferential organizational structure. The proposed organizational structure includes cooperative relationships, supervisory control relationships, and decision-making authorization relationships. In addition,by considering the influence on the intelligent support capabilities and the task execution reliability created by adjustable autonomy, it helps to build the proposed organizational structure designed with certain degree of flexibility to deal with the potential changes in the unpredictable battlefield environment. Simulation is conducted to confirm our design to be valid. And the method is still valid under different battlefield environments and interventions.

Revisiting the SCO in the Context of Its Enlargement

The membership expansion of the Shanghai Cooperation Organization brings positive effects but also gives rise to new uncertainties. In the face of evolving internal and external environment, it is necessary to understand the organization's original principles so that both old and new members can recognize the SCO's core values.

Interactive Model Used in Maintenance Planning of Facades Based on Virtual Reality Technology

A virtual reality model was created in order to help in the maintenance of exterior closures and interior finishes of walls in a building. It allows the visual and interactive transmission of information related to the physical behavior of the elements, defined as a function of the time variable. To this end, the basic knowledge of material most often used in walls, anomaly surveillance, techniques of rehabilitation, and inspection planning were studied. This information was included in a database that supports the periodic inspection needed in a program of preventive maintenance. The results are obtained interactively and visualized in the virtual environment itself. This work brings an innovative contribution to the field of maintenance supported by emergent technology.

Neuro-Behavioral Dynamic Prediction of Interpersonal Cooperation and Aggression

How to quickly predict an individual’s behavioral choices is an important issue in the field of human behavior research.Using noninvasive electroencephalography,we aimed to identify neural markers in the prior outcome-evaluation stage and the current option-assessment stage of the chicken game that predict an individual’s behavioral choices in the subsequent decision-output stage.Hierarchical linear modeling-based brain-behavior association analyses revealed that midfrontal theta oscillation in the prior outcome-evaluation stage positively predicted subsequent aggressive choices;also,beta oscillation in the current option-assessment stage positively predicted subsequent cooperative choices.These findings provide electrophysiological evidence for the three-stage theory of decision-making and strengthen the feasibility of predicting an individual’s behavioral choices using neural oscillations.