Bayesian networks + reinforcement learning: Controlling group emotion from sensory stimuli
شبکه های بیزی + یادگیری تقویتی : کنترل احساسات گروهی از محرک های حسی-2020
As communication technology develops, various sensory stimuli can be collected in service spaces. To enhance the service effectiveness, it is important to determine the optimal stimuli to induce group emo- tion in the service space to the target emotion. In this paper, we propose a stimuli control system to adjust the group emotion. It is a stand-alone system that can determine optimal stimuli by utility ta- ble and modular tree-structured Bayesian networks designed for emotion prediction model proposed in the previous study. To verify the proposed system, we collected data using several scenarios at a kinder- garten and a senior welfare center. Each space is equipped with sensors for collection and equipment for controlling stimuli. As a result, the system shows a performance of 78% in the kindergarten and 80% in the senior welfare center. The proposed method shows much better performance than other classifica- tion methods with lower complexity. Also, reinforcement learning is applied to improving the accuracy of stimuli decision for a positive effect on system performance.
Keywords: Adjusting emotion | Group emotion | Bayesian networks | Reinforcement Learning | IoT