Chang-Shing Lee

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Chang-Shing Lee [1]

Chang-Shing Lee,
a Taiwanese computer scientist and professor at National University of Tainan (NUTN). His major research interests are in ontology applications, knowledge management, capability maturity model integration (CMMI), semantic web, and artificial intelligence. He is also interested in intelligent agent, web services, fuzzy logic, genetic algorithm, and image processing. Chang-Shing Lee holds several patents on ontology engineering, document classificaton, image filtering and health care.


Chang-Shing Lee is co-programmer of the Go playing program MogoTW [2] [3], a joint project between the MoGo team and a Taiwanese team [4]. It shares code with MoGo.

FML-based Prediction

Abtract from FML-based Prediction Agent and Its Application to Game of Go [5]:

In this paper, we present a robotic prediction agent including a darkforest Go engine, a fuzzy markup language (FML) assessment engine, an FML-based decision support engine, and a 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 NUTN and OPU, for example, the number of 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 [6], 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 [7]:

This paper presents a semantic brain computer interface (BCI) agent with particle swarm optimization (PSO) based on a Fuzzy Markup Language (FML) for Go learning and prediction applications.  Additionally, we also establish an Open Go Darkforest (OGD) cloud platform with Facebook AI research (FAIR) open source Darkforest and ELF OpenGo AI bots [8]. 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 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


2007 ...

2010 ...

Chang-Shing Lee, Mei-Hui Wang, Yu-Jen Chen, Shi-Jim Yen (2013). Apply Fuzzy Markup Language to Knowledge Representation for Game of Computer Go.

2015 ...

External Links


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