Evolving Rule-Based Explainable Artificial Intelligence for Unmanned Aerial Vehicles

Abstract

In this paper, an explainable intelligence model that gives the logic behind the decisions unmanned aerial vehicle (UAV) makes when it is on a predefined mission and chooses to deviate from its designated path is developed. The explainable model is on a visual platform in the format of if-then rules derived from the Sugeno-type fuzzy inference model. The model is tested using the data recorded from three different missions. In each mission, adverse weather, conditions and enemy locations are introduced at random locations along the path of the mission. There are two phases to the model development. In the first phase, the Mamdani fuzzy model is used to create rules to steer the UAV along the designated mission and the rules of engagement when it encounters weather and enemy locations along and near its chosen mission. The data are gathered as UAV traverses on each mission. In the second phase, the data gathered from these missions are used to create a reverse model using a Sugeno-type fuzzy inference system based on the subtractive clustering in the data. The model has seven inputs (time, x-coordinate, y-coordinate, heading direction, engage in attack, continue mission, and steer UAV) and two outputs (weather conditions and distance from the enemy). This model predicts the outputs regarding the weather conditions and enemy positions whenever UAV deviates from the predefined path. The model is optimized with respect to the number of rules and prediction accuracy by adjusting subtractive clustering parameters. The model is then fine-tuned with ANFIS. The final model has six rules and root mean square error value that is less than 0.05. Furthermore, to check the robustness of the model, the Gaussian random noise is added to a UAV path, and the prediction accuracy is validated.INDEX TERMS Explainable artificial intelligence (XAI), fuzzy logic, ANFIS, unmanned aerial vehicle (UAV), subtractive clustering. I. INTRODUCTIONUnmanned Air Vehicles(UAVs) are used for many purposes including agriculture, industry, law enforcement, and defense. These autonomous systems have several advantages over manned aerial vehicles as not only they reduce expenses by avoiding human error, but they also save the lives of fighter jet pilots.The incoming generation of artificial intelligence(AI) systems are showing significant success through the use of various machine learning techniques. These systems offer a wide range of benefits when it comes to simplifying the lives of individuals as well as military operations. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of today's AI systems is limited by the inability of the machine to explain its decisions and actions to human users [1]-[3]. This is where the concept of Explainable Artificial Intelligence (XAI) comes in to play. XAI aims to create a suite of machine learning techniques that will produce more explainable models while maintaining a high level of learning performance (prediction accuracy)....

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