Genetically evolved action selection mechanism in a behavior-based system for target tracking

Abstract

The success of a behavior-based system relies largely on its Action Selection Mechanism (ASM) module, which is basically a behavior coordination method of either arbitration or command fusion type. Deciding on the right coordination method for ASM when executing a given mission in an arbitrary environment can be a huge obstacle. Providing the system with some kind of Artificial Intelligence (AI) to deal with the dynamics of a given task would be highly recommended. In this paper, an evolutionary process has been employed in a behavior-based system to generate a suitable ASM based on a system's mission scenario. A Genetic Algorithm (GA) is used to train the weights of a Multi-layer Perceptron (MLP) feed-forward artificial neural network in identifying a suitable formulation of ASM. Implementation of such systems in a target tracking mission has shown positive results. Depending on the mission scenario, the evolved ASM can dynamically manage the coordination method in order to achieve the overall system objective.