Lärande av beteendeträd för simulerade stridspiloter i spaningsuppdrag

Fighter pilots often find themselves in situations where they need to make quick decisions. Therefore an intelligent decision support system that suggests how the fighter pilot should act in a specific situation is vital. The aim of this project is to investigate and evaluate grammatical evolution paired with behaviour trees to develop a decision support system. This support system should control a simulated fighter pilot during an airborne reconnaissance mission. This thesis evaluates the complexity of the evolved trees and the performance, and robustness of the algorithm. Key factors were identified for a successful system: scenario, fitness function, initialisation technique and control parameters. The used techniques were decided based on increasing performance of the algorithm and decreasing complexity of the tree structures. The initialisation technique, the genetic operators and the selection functions performed well but the fitness function needed more work. Most of the experiments resulted in local maxima. A desired solution could only be found if the initial population contained an individual with a BT succeeding the mission. However, the implementation behaved as expected. More and longer simulations are needed to draw a conclusion of the performance based on robustness, when testing the evolved BT:s on different scenarios. Several methods were studied to decrease the complexity of the trees and the experiments showed a promising variation of complexity through the generations when the best fitness was fixed. A feature was added to the algorithm, to promote lower complexity when equal fitness value. The results were poor and implied that pruning would be a better fit after the simulations. Nevertheless, this thesis suggests that it is suitable to implement a decision support system based on grammatical evolution paired with behaviour trees as framework