Solving Battle Management/Command Control and Communication Problem using Modified BIONET

Abstract

This paper proposes and implements a neural architecture to solve the weapon allocation
problem in the multi-layer defense scenario using modified BIONET neural network architecture.
The presynaptic layer of the modified BIONET reduces the dimensionality of the principal state
equation by partitioning the state space. The post-synaptic layer of the modified BIONET includes
the perceptron Q-learning rule. The cortical layer incorporates L-learning scheme to provide
better exploration over action space. Thus, action selection is effectively made with quicker
convergence of training. The reward scheme in the reinforcement learning is obtained by
calculating the measure of probability of survival. The decision module has been enhanced by
incorporating the features corresponding to the battle weapons for effective representation of
the environment. Thus, the modified BIONET neural architecture is used to increase the efficiency
of assets saved in the simulation and the time complexity is reduced due to the state-space
partitioning scheme involved in the neural network. The proposed modified BIONET is
implemented in MATLAB and the percentage of assets saved is increased. Also, the training
time is drastically reduced. Thus, the modified BIONET resulted in saving more assets with faster
convergence of learning.