Machine learning has become a very popular approach for cybernetics systems, and it has always be... more Machine learning has become a very popular approach for cybernetics systems, and it has always been considered important research in the Computational Intelligence area. Nevertheless, when it comes to smart machines, it is not just about the methodologies. We need to consider systems and cybernetics as well as include human in the loop. The purpose of this article is as follows: (1) To integrate the open source Facebook AI Research (FAIR) DarkForest program of Facebook with Item Response Theory (IRT), to the new open learning system, namely, DDF learning system; (2) To integrate DDF Go with Robot namely Robotic DDF Go system; (3) To invite the professional Go players to attend the activity to play Go games on site with a smart machine. The research team will apply this technology to education, such as, playing games to enhance the children concentration on learning mathematics, languages, and other topics. With the detected brainwaves, the robot will be able to speak some words that are very much to the point for the students and to assist the teachers in classroom in the future.
Journal of Ambient Intelligence and Humanized Computing
This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificia... more This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students' learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software Engineering and learning English.
2020 International Symposium on Community-centric Systems (CcS), 2020
In this paper, we propose an AI-FML robotic agent for student learning behavior ontology construc... more In this paper, we propose an AI-FML robotic agent for student learning behavior ontology construction which can be applied in English speaking and listening domain. The AI-FML robotic agent with the ontology contains the perception intelligence, computational intelligence, and cognition intelligence for analyzing student learning behavior. In addition, there are three intelligent agents, including a perception agent, a computational agent, and a cognition agent in the AI-FML robotic agent. We deploy the perception agent and the cognition agent on the robot Kebbi Air. Moreover, the computational agent with the Deep Neural Network (DNN) model is performed in the cloud and can communicate with the perception agent and cognition agent via the Internet. The proposed AI-FML robotic agent is applied in Taiwan and tested in Japan. The experimental results show that the agents can be utilized in the human and machine co-learning model for the future education.
2011 IEEE Conference on Computational Intelligence and Games (CIG'11), 2011
In this paper, we will consider questions related to blindfolded play: (i) the impact (in various... more In this paper, we will consider questions related to blindfolded play: (i) the impact (in various conditions) of playing blindfolded in the level of Go players in 9x9 Go (ii) the influence of a visual support (the visual support is a board with no stone) (iii) which modifications are required for making a program strong in the blind variant of the game (and, somehow surprisingly, implementing a program for playing blind go is not equivalent to implementing a program for playing go) (iv) some conclusions on the rules of blind Go for making it interesting and pedagogically efficient.
2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Jul 1, 2018
An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup... more An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.
2019 IEEE Congress on Evolutionary Computation (CEC), 2019
This paper applies a genetic algorithm and fuzzy markup language to construct a human and smart m... more This paper applies a genetic algorithm and fuzzy markup language to construct a human and smart machine cooperative learning system on game of Go. The genetic fuzzy markup language (GFML)-based Robot Agent can work on various kinds of robots, including Palro, Pepper, and TMU's robots. We use the parameters of FAIR open source Darkforest and OpenGo AI bots to construct the knowledge base of Open Go Darkforest (OGD) cloud platform for student learning on the Internet. In addition, we adopt the data from AlphaGo Master's sixty online games as the training data to construct the knowledge base and rule base of the co-learning system. First, the Darkforest predicts the win rate based on various simulation numbers and matching rates for each game on OGD platform, then the win rate of OpenGo is as the final desired output. The experimental results show that the proposed approach can improve knowledge base and rule base of the prediction ability based on Darkforest and OpenGo AI bot with various simulation numbers.
This paper presents a semantic brain computer interface (BCI) agent with particle swarm optimizat... more 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. 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 desi...
Machine learning has become a very popular approach for cybernetics systems, and it has always be... more Machine learning has become a very popular approach for cybernetics systems, and it has always been considered important research in the Computational Intelligence area. Nevertheless, when it comes to smart machines, it is not just about the methodologies. We need to consider systems and cybernetics as well as include human in the loop. The purpose of this article is as follows: (1) To integrate the open source Facebook AI Research (FAIR) DarkForest program of Facebook with Item Response Theory (IRT), to the new open learning system, namely, DDF learning system; (2) To integrate DDF Go with Robot namely Robotic DDF Go system; (3) To invite the professional Go players to attend the activity to play Go games on site with a smart machine. The research team will apply this technology to education, such as, playing games to enhance the children concentration on learning mathematics, languages, and other topics. With the detected brainwaves, the robot will be able to speak some words that are very much to the point for the students and to assist the teachers in classroom in the future.
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2017
In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-... more In this paper, we demonstrate the application of Fuzzy Markup Language (FML) to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an FML-based Human–Machine Cooperative System (FHMCS) for the game of Go. The proposed FDAA comprises an intelligent decision-making and learning mechanism, an intelligent game bot, a proximal development agent, and an intelligent agent. The intelligent game bot is based on the open-source code of Facebook’s Darkforest, and it features a representational state transfer application programming interface mechanism. The proximal development agent contains a dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket engine and a summarization agent that is based on the estimated win rate, real-time simulation number, and matching degree of predicted moves. Additionally, the FML for player performance evaluation and linguistic descriptions for g...
The Google DeepMind challenge match in March 2016 was a historic achievement for computer Go deve... more The Google DeepMind challenge match in March 2016 was a historic achievement for computer Go development. This article discusses the development of computational intelligence (CI) and its relative strength in comparison with human intelligence for the game of Go. We first summarize the milestones achieved for computer Go from 1998 to 2016. Then, the computer Go programs that have participated in previous IEEE CIS competitions as well as methods and techniques used in AlphaGo are briefly introduced. Commentaries from three high-level professional Go players on the five AlphaGo versus Lee Sedol games are also included. We conclude that AlphaGo beating Lee Sedol is a huge achievement in artificial intelligence (AI) based largely on CI methods. In the future, powerful computer Go programs such as AlphaGo are expected to be instrumental in promoting Go education and AI real-world applications.
2011 IEEE Conference on Computational Intelligence and Games (CIG'11), 2011
It is known that in chess, random positions are harder to memorize for humans. We here reproduce ... more It is known that in chess, random positions are harder to memorize for humans. We here reproduce these experiments in the Asian game of Go, in which computers are much weaker than humans. We survey families of positions, discussing the relative strength of humans and computers, and then experiment random positions. The result is that computers are at the best amateur level for random positions. We also provide a protocol for generating interesting random positions (avoiding unfair situations).
International Journal of Approximate Reasoning, 2008
This paper presents an ontology-based intelligent decision support agent (OIDSA) to apply to proj... more This paper presents an ontology-based intelligent decision support agent (OIDSA) to apply to project monitoring and control of capability maturity model integration (CMMI). The OIDSA is composed of a natural language processing agent, a fuzzy inference agent, and a performance decision support agent. All the needed information of the OIDSA, including the CMMI ontology and the project personal ontology, is stored in an ontology repository. In addition, the natural language processing agent, based on the Chinese Dictionary, periodically collects the information of the project progress from project members to analyze the features of the Chinese terms for semantic concept clustering. Next, the fuzzy inference agent computes the similarity of the planned progress report and actual progress report, based on the CMMI ontology, the project personal ontology, and natural language processing results. Finally, the performance decision support agent measures the completed percentage of the progress for each project member. The results provided by the OIDSA are sent to the project manager for evaluating the performance of each project member. The experimental results show that the OIDSA can work effectively for project monitoring and control of CMMI.
IEEE Transactions on Computational Intelligence and AI in Games, 2010
This paper presents the recent technical advances in Monte-Carlo Tree Search for the Game of Go, ... more This paper presents the recent technical advances in Monte-Carlo Tree Search for the Game of Go, shows the many similarities and the rare differences between the current best programs, and reports the results of the computer-Go event organized at FUZZ-IEEE 2009, in which four main Go programs played against top level humans. We see that in 9x9, computers are very close to the best human level, and can be improved easily for the opening book; whereas in 19x19, handicap 7 is not enough for the computers to win against top level professional players, due to some clearly understood (but not solved) weaknesses of the current algorithms. Applications far from the game of Go are also cited. Importantly, the first ever win of a computer against a 9th Dan professional player in 9x9 Go occurred in this event.
Monte-Carlo Tree Search (MCTS) is a very efficient recent technology for games and planning, part... more Monte-Carlo Tree Search (MCTS) is a very efficient recent technology for games and planning, particularly in the high-dimensional case, when the number of time steps is moderate and when there is no natural evaluation function. Surprisingly, MCTS makes very little use of learning. In this paper, we present four techniques (ontologies, Bernstein races, Contextual Monte-Carlo and poolRave) for learning agents in Monte-Carlo Tree Search, and experiment them in difficult games and in particular, the game of Go.
Computational Intelligence (CI), which includes fuzzy logic (FL), neural network (NN), and evolut... more Computational Intelligence (CI), which includes fuzzy logic (FL), neural network (NN), and evolutionary computation (EC), is an imperative branch of artificial intelligence (AI). As a core technology of AI, it plays a vital role in developing intelligent systems, such as games and game engines, neural-based systems including a variety of deep network models, evolutionarybased optimization methods, and advanced cognitive techniques. The 2021 IEEE CIS Summer School on CI for High-School Student Learning was held physically at the JanFuSun Resort Hotel, Taiwan, and virtually on Zoom, on August 10-12, 2021. The main contents of the Summer School were lectures focused on the basics of FL, NN, and EC and the workshop on AIoT (Artificial Intelligence of Things). Invited speakers gave nine courses covering topics like CI real-world applications, fundamentals of FL, and the introduction to NN and EC. The 2021 Summer School was supported by the 2021 IEEE CIS High School Outreach Subcommittee. We also invited students and teachers of high and elementary schools from Taiwan, Japan, and Indonesia. They attended the school and participated in AIoT workshop, gaining experience in applications of AIoT-FML learning tools. According to the short report and feedback from the involved students and teachers, we find out that most participants have quickly understood the principles of CI, FL, NN, and EC. In addition, one of the teachers sent the following remark to the organizers: "This is a great event to introduce students to computational intelligence at a young age, stimulate them to be involved in rapidly evolving fields, and foster participation in future research adventures." Index Terms-Computational Intelligence, AIoT, AI-FML, Human and Robot Co-Learning I.
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020
In this paper, we propose an AI-FML agent for robotic game of Go and AIoT real-world co-learning ... more In this paper, we propose an AI-FML agent for robotic game of Go and AIoT real-world co-learning applications. The fuzzy machine learning mechanisms are adopted in the proposed model, including fuzzy markup language (FML)-based genetic learning (GFML), eXtreme Gradient Boost (XGBoost), and a seven-layered deep fuzzy neural network (DFNN) with backpropagation learning, to predict the win rate of the game of Go as Black or White. This paper uses Google AlphaGo Master sixty games as the dataset to evaluate the performance of the fuzzy machine learning, and the desired output dataset were predicted by Facebook AI Research (FAIR) ELF Open Go AI bot. In addition, we use IEEE 1855 standard for FML to describe the knowledge base and rule base of the Open Go Darkforest (OGD) prediction platform in order to infer the win rate of the game. Next, the proposed AI-FML agent publishes the inferred result to communicate with the robot Kebbi Air based on MQTT protocol to achieve the goal of human and smart machine co-learning. From Sept. 2019 to Jan. 2020, we introduced the AI-FML agent into the teaching and learning fields in Taiwan. The experimental results show the robots and students can co-learn AI tools and FML applications effectively. In addition, XGBoost outperforms the other machine learning methods but DFNN has the most obvious progress after learning. In the future, we hope to deploy the AI-FML agent to more available robot and human co-learning platforms through the established AI-FML International Academy in the world.
In order to stimulate the development and research in computer Go, several Taiwanese Go players, ... more In order to stimulate the development and research in computer Go, several Taiwanese Go players, including three professional Go players and four amateur Go players, were invited to play against the famous computer Go program, MoGo, in the Taiwan Open 2009. The MoGo program combines the online game values, offline values extracted from databases, and expert rules defined by Go expert that shows an excellent performance in the games. The results reveal that MoGo can reach the level of 3 Dan in Taiwan amateur Go environment. But there are still some drawbacks for MoGo that should be solved, for example, the weaknesses in semeai and how to flexibly practice the human knowledge through the embedded opening books. In this paper, a new game record ontology for computer Go knowledge management is proposed to solve the problems that MoGo is facing. It is hoped that the advances in intelligent agent and ontology model can provide much more knowledge to make a progress in computer Go and achieve as much as computer chess or Chinese chess in the future. I.INTRODUCTION omputer Go has proved to be a troublesome game in that the best computer players still play at the level of a good novice [1]. Additionally, Go remains an excellent challenge for computer science research; however Monte Carlo methods have very recently shown significant promise, especially for small versions of the game such as 9 × 9 games. Werf et al. [2] presented a search-based approach for playing Go on small boards. Bouzy and Cazenave [3] presented an AI-oriented survey of computer Go. Computer Go has been developing for the past several years. In 1998, Martin Muller won despite 29 handicap stones against the computer Go "Many Faces of Go" [4]. In August 2008, the program "MoGo" (http://www.lri.fr/~teytaud/mogo.html) won with an advantage of "only" 9 handicap stones against top-level human players in 19 × 19 Go-Myung-Wan Kim, who won the 2008 US Open and was a Korean 8 th Dan Pro (8P).
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Papers by Mei-Hui Wang