Okayama University


Intelligent Design


In the division of Intelligent Design, our research focuses on developing technologies that efficiently search for desired information and discover valuable insights from the vast amount of Big Data available on the Web, utilizing artificial intelligence and other related approaches. Additionally, we explore digital libraries that enable users to seamlessly navigate between the virtual world of cyberspace, connected by the Internet, and the physical reality. Our research also includes stream delivery technologies for multimedia information, including audio and video, with a particular emphasis on the integration of Internet broadcasting and data communications.

  • Prof. OHTA Manabu
  • E-mail: ohta@(okayama-u.ac.jp)
  • Web Information Retrieval/Web Mining/Digital Library


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Cyber-physical browser for research papers

Even with the prevalence of electronic documents, there are still many opportunities to read paper documents. For this reason, we are developing a paper browser, as shown in the photo, which utilizes a tablet device’s camera and character recognition technology to allow users to read physical papers printed on paper, supplemented by relevant information in cyberspace. Our ultimate aim is to make this kind of reading possible in the future by simply wearing specialized glasses.



  • Assoc. Prof. GOTOH Yusuke
  • E-mail: y-gotoh@(okayama-u.ac.jp)
  • Streaming Delivery in Broadcasting Environments/Spatial Computing

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In broadcasting environments, we propose a scheduling technology that enables many users to immediately watch the video, and evaluate its performance by implementing it in a streaming delivery system constructed in our laboratory. In spatial computing, we propose a new technology that enables users to enjoy a variety of services based on their location data.



  • Asst. Prof. UWANO Fumito
  • E-mail: ph7u1700@(okayama-u.ac.jp)
  • Reinforcement Learning/Distributed Artificial Intelligence


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Example of two navigation robots

My work focuses on intelligence design to duplicate high-level strategy using Reinforcement Learning (RL), an Artificial Intelligence technique. Concretely, I assume to introduce RL to an agent who senses information to manipulate its actuator like a robot. I aim to establish how to create knowledge encoding the learning results and utilize, and enable agents to learn cooperative policy with sparse information in a Multi-agent system where there are two or more agents.