# Time-Varying Preference Bandits for Robot Behavior Personalization
## 9 Conclusions  
Our proposed algorithm introduces a novel approach to address time-varying preferences using discounted likelihood. Then, our theoretical analysis establishes that DPB demonstrates sub-linear cumulative regret under preference changes occurring less than \(O(T^{1/2})\) times. Experimental outcomes highlight the adaptiveness of our framework  
Figure 5: Real-world user studies comparing DPB with BA (batch active preference-based learning) _greedy_[5] in a changing environment scenario. Results of **Q1**\((p<0.05)\), **Q2**\((p<0.05)\), and **Q3** from user studies are shown (mean \(\pm\) std over 9 participants). The dashed vertical line in (c) means the timing of adding the wine glass to the desk.  
Figure 6: Simulation user studies comparing DPB with BA (batch active preference-based learning) _greedy_[5] to test the time-varying scenario with robot behavior adaptation over repeated interactions. Results of **Q1**\((p<0.001)\), **Q2**\((p<0.001)\), **Q3**, and **Response time** from user studies are shown (mean \(\pm\) std over 10 participants).  
compared to previous methods in handling time-varying user preferences. Particularly, our DPB method effectively minimizes cumulative regret, while other approaches struggle in this regard. User studies further validate the competitiveness of DPB in time-varying environments with environmental changes and robot behavior adaptation over repeated interactions.  
In robotics, addressing the time-varying preferences of users is crucial, as their expectations can shift depending on the context or environment. Robotic systems are required to perform a wide range of actions across diverse scenarios, adapting their behavior to evolving human needs. For instance, in autonomous driving systems, the DPB method enables vehicles to modify their driving styles--such as acceleration, braking, or lane-changing. Passengers preferring a smoother, more conservative ride may find the vehicle adjusting its behavior accordingly, while those prioritizing efficiency could benefit from optimized travel times. This continuous learning and adaptation enhance the personalization and comfort of autonomous systems. It would be interesting to consider additional factors such as emotions or social contexts that influence preference changes. Exploring these aspects and developing a unified model capable of perceiving user states and learning preferences across diverse users represent promising directions for future research.  
Conceptualization, C.K., J.L. and K.L.; methodology, K.L.; software, J.L.; validation, C.K.; writing--original draft preparation, C.K., J.L. and K.L.; writing--review and editing, C.K., E.K. and K.L.; supervision, E.K. and K.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Chung-Ang University Graduate Research Scholarship in 2022 and the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2023-00211357, Smart Assembler: Robot Active Learning for Unseen Parts Assembly).  
Ethical review and approval were waived for this study due to the minimal-risk nature of the research, as participants were not exposed to any physical, psychological, or emotional harm. The study involved passive observation of a robot's pick-and-place process and subjective feedback through non-invasive questionnaires. No personal or identifiable information was collected, and the research did not include vulnerable populations or sensitive topics.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.