Networks have gotten bigger recently, and users have a more difficult time finding the information that they want. The use of mobile agents to help users effectively retrieve information has garnered a lot of attention. In this paper, we propose an agent control method for time constrained information retrieval. We pay attention to the highest past score gained by the agents and control the agents with the expectation of achieving better scores. Using computer simulations, we confirmed that our control method gave the best improvement over the whole network while reducing the overall variance. From these results, we can say that our control method improves the quality of information retrieved by the agent.

Video inpainting is a task of filling missing regions in videos. In this task, it is important to efficiently use information from other frames and generate plausible results with sufficient temporal consistency. In this paper, we present a video inpainting method jointly using affine transformation and deformable convolutions for frame alignment. The former is responsible for frame-scale rough alignment and the latter performs pixel-level fine alignment. Our model does not depend on 3D convolutions, which limits the temporal window, or troublesome flow estimation. The proposed method achieves improved object removal results and better PSNR and SSIM values compared with previous learning-based methods.