Evolving adaptive and interpretable decision trees for cooperative submarine search

System upgrades in unmanned systems have made Unmanned Aerial Vehicle (UAV)-based patrolling and monitoring a preferred solution for ocean surveillance. However, dynamic environments and large-scale deployments pose significant challenges for efficient decision-making, necessitating a modular multi-agent control system. Deep Reinforcement Learning (DRL) and Decision Tree (DT) have been utilized for these complex decision-making tasks, but each has its limitations: DRL is highly adaptive but lacks interpretability, while DT is inherently interpretable but has limited adaptability. To overcome these challenges, we propose the Adaptive Interpretable Decision Tree (AIDT), an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes. We first construct a Markov decision process (MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method. Specifically, we use the heat map as a state variable to address the issue of multi-agent input state proliferation. Next, we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance. Additionally, we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency. To further improve the generalization capability of the decision tree, we apply a normalization method to ensure consistent processing of input states. Finally, we validate the proposed algorithm in different environmental settings, and the results demonstrate both its adaptability and interpretability.