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=MogoTW=
Chang-Shing Lee is co-programmer of the [[Go]] playing program ''MogoTW'' <ref>[http://www.computer-go.info/db/oprog.php?a=MogoTW MogoTW]</ref> <ref>[http://www.computer-go.info/db/operson.php?a=Lee%2C+Chang-Shing Details of Programmer: Lee, Chang-Shing - Co-programmer of MogoTW]</ref>, a joint project between the [https://www.game-ai-forum.org/icga-tournaments/program.php?id=515 MoGo] team and a Taiwanese team <ref>[http://www.lri.fr/~teytaud/mogo.html MoGo: a software for the Game of Go]</ref>. It shares code with MoGo.
=FML-based Prediction=
Abtract from ''FML-based Prediction Agent and Its Application to Game of Go'' <ref>[[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Chia-Hsiu Kao]], [[Sheng-Chi Yang]], [[Yusuke Nojima]], [[Ryosuke Saga]], [[Nan Shuo]], [[Naoyuki Kubota]] ('''2017'''). ''FML-based Prediction Agent and Its Application to Game of Go''. [https://arxiv.org/abs/1704.04719 arXiv:1704.04719]</ref>:
In this paper, we present a robotic prediction agent including a [https://en.wikipedia.org/wiki/Darkforest darkforest] [[Go]] engine, a [https://en.wikipedia.org/wiki/Fuzzy_markup_language fuzzy markup language] (FML) assessment engine, an FML-based decision support engine, and a [[Robots|robot engine]] for game of Go application. The knowledge base and rule base of FML assessment engine are constructed by referring the information from the darkforest Go engine located in [https://en.wikipedia.org/wiki/National_University_of_Tainan NUTN] and [https://en.wikipedia.org/wiki/Osaka_Prefecture_University OPU], for example, the number of [[Monte-Carlo Tree Search|MCTS]] simulations and winning rate prediction. The proposed robotic prediction agent first retrieves the database of Go competition website, and then the FML assessment engine infers the winning possibility based on the information generated by darkforest Go engine. The FML-based decision support engine computes the winning possibility based on the partial game situation inferred by FML assessment engine. Finally, the robot engine combines with the human-friendly robot partner PALRO <ref>[https://palro.jp/en/ PALRO is a robot who cares]</ref>, produced by FujiSoft Incorporated, to report the game situation to human Go players. Experimental results show that the FML-based prediction agent can work effectively.
=PFML-based BCI Agent=
Abtract from ''PFML-based Semantic BCI Agent for Game of Go Learning and Prediction'' <ref>[[Chang-Shing Lee]], [[Mei-Hui Wang]], [[Li-Wei Ko]], [[Bo-Yu Tsai]], [[Yi-Lin Tsai]], [[Sheng-Chi Yang]], [[Lu-An Lin]], [[Yi-Hsiu Lee]], [[Hirofumi Ohashi]], [[Naoyuki Kubota]], [[Nan Shuo]] ('''2019'''). ''PFML-based Semantic BCI Agent for Game of Go Learning and Prediction''. [https://arxiv.org/abs/1901.02999 arXiv:1901.02999]</ref>:
This paper presents a semantic [https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface brain computer interface] (BCI) agent with [https://en.wikipedia.org/wiki/Particle_swarm_optimization particle swarm optimization] (PSO) based on a [https://en.wikipedia.org/wiki/Fuzzy_markup_language Fuzzy Markup Language] (FML) for [[Go]] [[Learning|learning]] and prediction applications. Additionally, we also establish an Open Go [https://en.wikipedia.org/wiki/Darkforest Darkforest] (OGD) cloud platform with Facebook AI research (FAIR) open source Darkforest and ELF OpenGo AI bots <ref>[https://ai.facebook.com/blog/open-sourcing-new-elf-opengo-bot-and-go-research/ Open-sourcing a new ELF OpenGo bot and related Go research], February 13, 2019</ref>. The Japanese robot Palro will simultaneously predict the move advantage in the board game Go to the Go players for reference or learning. The proposed semantic BCI agent operates efficiently by the human-based BCI data from their [https://en.wikipedia.org/wiki/Neural_oscillation brain waves] and machine-based game data from the prediction of the OGD cloud platform for optimizing the parameters between humans and machines. Experimental results show that the proposed human and smart machine co-learning mechanism performs favorably. We hope to provide students with a better online learning environment, combining different kinds of handheld devices, robots, or computer equipment, to achieve a desired and intellectual learning goal in the future.
=Selected Publications=
