Alpha C2–An Intelligent Air Defense Commander Independent of Human Decision-Making

Abstract

The ultimate goal of military intelligence is to equip the command and control (C2) system with the decision-making art of excellent human commanders and to be more agile and stable than human beings. Intelligent commander Alpha C2 solves the dynamic decision-making problem in the complex scenarios of air defense operations using a deep reinforcement learning framework. Unlike traditional C2 systems that rely on expert rules and decision-making models, Alpha C2 interacts with digital battlefields close to the real world and generates learning data. By integrating the states of multiple parties as input, a gated recurrent unit network is used to introduce historical information, and an attention mechanism selects the object of action, making the output decision more reliable. Without learning human combat experience, the neural network is trained in fixed-and random-strategy scenarios based on a proximal policy optimization algorithm. Finally, 1,000 rounds of offline confrontation were conducted on a digital battlefield, whose results show that the generalization ability of Alpha C2 trained using a random strategy is better, and that it can defeat an opponent with a higher winning rate than an Expert C2 system (72% vs 21%). The use of resources is more reasonable than Expert C2, reflecting the flexible and changeable art of command. INDEX TERMS Intelligent decision-making, deep reinforcement learning, Alpha C2, expert system.

On games and simulators as a platform for development of artificial intelligence for command and control

“…With respect to simulators built using existing game engines, Sun et al 43 applied deep reinforcement learning to the command and control of air defense operations. Digital battlefield environment based on Unreal Engine was developed for reinforcement learning training process.…”

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

“…The authors in [34] used deep q-neural networks to obtain combat strategies in an attack-defense pursuit-warfare of multiple UCAVs in a simplified environment. Alpha C2, an intelligent Air Defense Commander operations using a deep reinforcement learning framework was presented in [10]. The proposed system makes combat decision independent of human decision-making.…”

“…For instance, consider the continues variable of 'velocity, distance, heading' of a detected target. If we are to quantize them to 3 states (fuzzy sets) each as shown in Equation 8- (10). The indexes are 1, 2, and 3 from low to high.…”

“…To assist with the ever more complex and rapidly changing combat environment, combat systems require access to real-time adaptable control systems that can handle uncertain information to provide intelligent decision-making when executing combat tasks. Research work in this area seeks to realize the effective utilization of combat resources by minimizing defensive assets loss, amount of resource utilized during interception, and maximizing the effective kill probability while operating within the multiple constraints of the operating platforms [5,[7][8][9][10]. Effective combat systems in a multi-agent setting must be able to solve conflicts of interest and optimize resource allocation.…”

An Intelligent Mission Planning Model for the Air Strike Operations against Islands Based on Neural Network and Simulation

“…In recent years, researchers have been trying to use artificial intelligence for planning in order to reduce the burden of commanders and improve speed and efficiency. But artificial intelligence is mainly used for tactical decision making, such as target classification [1][2][3], behavior recognition [4], threat assessment and target assignment [5,6], fire support of the maneuver operation [7], and so on. For combat-level mission planning, there is little research on the application of machine learning because it is difficult to obtain training datasets.…”