Çok kollu haydutlarda eksik tercihler ve gürbüz yeterlilik

This thesis studies sequential decision-making in multi-armed bandits (MAB) in two settings: when there are multiple objectives to consider and preferences among actions are non-total, and when actions are subject to adversarial perturbations. In the first part, we introduce PaVeBa, a pure-exploration algorithm for vector-valued rewards under incomplete preferences, using cone-induced comparisons instead of scalarizations. We analyze its behavior and validate it empirically against strong baselines. To support research and reproducibility in this area, we also develop VOPy, a Python library that implements PaVeBa and a broader suite of vector-optimization algorithms and tools. In the second part, we investigate robust satisficing under adversarial attacks. Building on existing robust satisficing formulations (e.g., stability radius and fragility-based), we design a Thompson-sampling variant of an algorithm family and demonstrate that it achieves reliable satisficing performance under targeted perturbations.