Determining possible avenues of approach using ANTS

Threat assessment is an important part of level 3 data fusion. Here we study a subproblem of this, worst-case risk assessment. Inspired by agent-based models used for simulation of trail formation for urban planning, we use ant colony optimization (ANTS) to determine possible avenues of approach for the enemy, given a situation picture. One way of determining such avenues would be to calculate the potential field'' caused by placing sources at possible goals for the enemy. This requires postulating a functional form for the potential, and also takes long time. Here we instead seek a method for quickly obtaining an effective potential. ANTS, which has previously been used to obtain approximate solutions to various optimization problems, is well suited for this. The output of our method describes possible avenues of approach for the enemy, i.e, areas where we should be prepared for attack. (The algorithm can also be run reversed’’ to instead get areas of opportunity for our forces to exploit.) Using real geographical data, we found that our method gives a fast and reliable way of determining such avenues. Our method can be used in a computer-based command and control system to replace the first step of human intelligence analysis.