AI and Technical Game Research

For artificial intelligence researchers, video games offer an ideal testing ground due to several distinct advantages. Games present a self-contained problem space where all possible events, variables, and outcomes are known. Additionally, they allow for the generation of large data sets through randomized gameplay, with deterministic outcomes thanks to their predictable and controlled environments [towardsdatascience.com]. Throughout the history of AI, many powerful algorithms were initially developed and tested within the realm of games before being adapted for broader applications. This is why I chose to deepen my understanding of advanced AI techniques through technical game research.

I had the privilege of working at the Intelligent Computer Entertainment Lab (ICE Lab) at Ritsumeikan University, a renowned laboratory recognized for its contributions to technical game research [kmjn.org/game-rankings]. My involvement included several projects exploring the applications of games and entertainment media. In my own research, I focused on developing game AI, analyzing human movement (motion analysis), bio-information (health assessment), and behavior (behavioral modeling). Some of these projects involved implementing AI based on the Monte Carlo Tree Search (MCTS) algorithm, which also powered AlphaGo. Other projects centered around analyzing and recognizing player behavior to induce behavior changes, enhancing both the user experience and overall well-being.

Within this academic domain, I have undertaken various roles, including:

• Part-time lecturer teaching “Theory of AI” and “AI for Games,” providing hands-on training at Bangkok University since 2018.
• Technical Committee member for Entertainment and Gaming (ENT)
• Member of the Ritsumeikan Center for Game Studies
• Program committee member for the IEEE 7th International Conference on Serious Games and Applications for Health (SeGAH) from 2019 to 2021
• Organizing member of the Fighting Game AI Competition at the IEEE Conference on Games (CoG, formerly CIG) from 2016 to 2021, a premier conference in the field of game AI
• Organizing committee member, Webmaster, Graphic Designer, and Special Session Chair at IEEE CoG 2020
• TPC member for the IEEE 8th Global Conference on Consumer Electronics (GCCE) in 2019 and 2020
• Reviewer for numerous journals and conferences to date.

Health Monitoring System

Health monitoring systems provide alternatives to traditional healthcare by reducing costs, improving treatment processes, and enabling remote healthcare services.

My master’s thesis, titled “Office Worker Syndrome Monitoring Using Kinect,” proposed a system designed to mitigate health risks for office workers who spend long hours at computers. The system used data mining classification models to detect extended sitting periods and mathematical models to identify unhealthy postures. This study involved the development of algorithms to assess health risk levels, design health data visualization dashboards, and generate health summary reports. Additionally, a microcontroller device was created to serve as a health monitoring assistant for system users. The project was presented at two international conferences, showcased during a campus visit by members of the Ministry of Information and Communication Technology of Thailand, and featured on national television on the ScienceTech program on ThaiPBS TV on May 15, 2014.

More than half of the papers I published with the ICE Lab focused on health promotion through video games and entertainment media. Games for Health (G4H) is an emerging field that examines the potential of video games in promoting health, a topic that continues to gain significant attention. In our lab, we address health in its entirety, as defined by the World Health Organization, which includes physical, mental, and social well-being [WHO, 1948]. Our research has explored the use of games to address issues such as depression, dementia, and the assessment of driving abilities in post-stroke patients (in collaboration with hospitals).

Data Analytics

Data is undoubtedly one of the most invaluable and influential assets in today’s modern world. With this belief, I have cultivated a profound interest in the design of systems for data collection and utilization. My expertise extends across various facets of data analysis, encompassing a diverse range of techniques such as statistical tests, data mining, machine learning, and deep learning. I possess comprehensive skills in designing databases, constructing data pipelines, developing machine learning algorithms, and generating comprehensive reports—essentially, I specialize in designing the entire data ecosystem.

• • My journey with data analysis began early, as I earned the 1st Runner-up award in the “IT Champion 2008: Data Analysis and Presentation Using SPSS” competition while pursuing my bachelor’s degree at the Vincent Mary School of Science and Technology. My proficiency with SPSS dates back to my high school days. Subsequently, I delved into the realm of statistics during my bachelor’s studies and advanced statistics during my research work. During my tenure at Ritsumeikan, I provided guidance to research students on the intricacies of designing systems for data collection, determining the necessary data for experiments, and selecting appropriate methods for data analysis.
  • During my master’s studies, my supervisor, an esteemed expert in data mining, mentored me in various data mining techniques. Classification played a central role in my master’s thesis, where I refined my skills in this area.
  • I also developed significant expertise in database technology throughout my academic journey. I enrolled in several database courses and gained practical experience, becoming well-versed in database and data pipeline design. My experience covers databases such as MySQL, Firebase, IBM Db2, and XML databases. Additionally, I honed my skills in advanced SQL commands and query optimization through elective courses during my master’s studies, applying them to real-world applications.

My experience extends across a broad spectrum of data types, including:

• • Log data from games and entertainment systems.
  • Data related to human movement, emotions (extracted via facial expressions, brainwaves, etc.), biosignals, and health-related information.
  • Data from retail businesses, e-learning systems, real estate, and stock markets.
  • Image data analysis.
  • Text and audio/speech data analysis for sentiment analysis.

Business and Investment

“My doctoral degree is in Engineering, my master’s is in Science, and my bachelor’s is in Business. This unique combination gives me a comprehensive understanding of business, empowering me to leverage data in building effective systems.”

I hold a Bachelor of Business Administration (BBA) with a major in Business Information Systems (BIS) from Assumption University of Thailand (AU). Through this program, I developed essential business skills in areas such as finance, accounting, and marketing. My focus was on e-commerce and data analysis for marketing, and I gained practical experience in investment along the way.

There were several key factors that initially led me to pursue a degree in business. In high school, I was awarded two scholarships offering different paths for my bachelor’s studies at AU: one for BBA and another for a Bachelor of Science in Information Technology. I chose the BBA program because of AU’s strong reputation as Thailand’s premier business school with a comprehensive English curriculum. At the time, I was drawn to the rapidly growing field of e-commerce and pursued online retailing as a hobby. BIS seemed like the ideal major to equip me with the knowledge and skills needed to succeed in this evolving business landscape. Additionally, this was the era of online stock trading and the early stages of various investment funds, which sparked my interest in finance and accounting—foundational pillars of business. I believed that understanding investment principles, such as analyzing company financials and mastering funding mechanisms, would be invaluable long term. Throughout my studies, I refined my ability to use data for business applications and became proficient in programming across multiple platforms, including desktop, mobile, web, and microcontrollers. I completed my bachelor’s degree with honors in just three and a half years.

With the belief that data analytics and AI would drive the future, I decided to pursue both master’s and doctoral degrees in this field. My bachelor’s education provided me with crucial business insights and the skills to utilize data for business optimization. My master’s studies further deepened my expertise in advanced data analysis for more effective data-driven strategies, while my doctoral research focused on applying data analysis techniques to create artificial intelligence, ultimately developing fully automated data ecosystems.