The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments

Lee, Chang-Shing; Wang, Mei-Hui; Chaslot, Guillaume; Hoock, Jean‐Baptiste; Rimmel, Arpad; Teytaud, Olivier; Tsai, Shang-Rong; Hsu, Shun-Chin; Hong, Tzung-Pei

doi:10.1109/tciaig.2009.2018703

Cited by 85 publications

(1 citation statement)

References 25 publications

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“…Literature [1] promotes the development of computer Go through the holding of computational intelligence contest. Experts have analyzed the four attributes of Go: weakness, time, game behavior, and main advantages, so as to further develop and study the field of computer Go in computational intelligence to improve the skills of computer Go.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

Bai¹,

Wang²,

Zhang³

2022

Wireless Communications and Mobile Computing

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At present, college students’ innovation and entrepreneurship has gradually become a hot topic nowadays, and the popularity of related knowledge of college students’ innovation and entrepreneurship is getting higher and higher, and more college students have joined in independent innovation and entrepreneurship. In order to alleviate a series of problems in college students’ innovation and entrepreneurship, we design an SD model under computational intelligence to put forward corresponding solutions to the problems faced in innovation and entrepreneurship, through the establishment of relevant models and experiments to verify that SD model is based on computational intelligence to optimize the design of college students’ innovation and entrepreneurship system. We know that the dimensions of tools include three dimensions: supply, demand, and environment. With the change of time, college students’ choice of tools for innovation and entrepreneurship has developed from a single supply tool to a multidimensional tool choice. Through the establishment of SD computational intelligence model to optimize the innovation and entrepreneurship system of college students, through the survey data, it can be seen that the prediction error value of SD model is about 9% and less than 10%, which is within the negligible error range, indicating that SD model has an optimization effect on the innovation and entrepreneurship system of college students. According to the survey, the three dimensions of capital investment, experience, and education level have an impact on college students’ innovation and entrepreneurship, accounting for 45%, 25%, and 30%, respectively. By comparing GW model, FIT model, and PBL model with SD model proposed in this paper, the data of precision, recall rate, accuracy rate, and F1 are compared and analyzed under the condition of changing the three dimensions of capital investment, experience, and education level, so as to select the best optimization model of innovation and entrepreneurship system for computational intelligence college students. Experiments show that when the capital investment increases by 10%, the relevant data of SD model is the best, with accuracy of 0.8761, recall rate of 0.9563, accuracy rate of 0.8972, and F1 of 0.8773; When the experience is increased by 10%, the optimization efficiency of SD model is the best, with its accuracy of 0.8660, recall rate of 0.9462, accuracy rate of 0.8871, and F1 of 0.8672. When the education level is increased by 10%, the optimization efficiency of SD model is the best, with accuracy of 0.8921, recall rate of 0.9762, accuracy rate of 0.8762, and F1 of 0.8861.

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Section: Introductionmentioning

confidence: 99%

Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

Bai¹,

Wang²,

Zhang³

2022

Wireless Communications and Mobile Computing

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AI for General Strategy Game Playing

Nielsen

Jensen

Mahlmann

et al. 2014

Handbook of Digital Games

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List of Figures vii List of Tables ix Acknowledgments xi AI for General Strategy Game Playing 13 10.1 Introduction 13 10.1.1 Strategy games 13 10.1.2 AI for board games 15 10.1.3 AI for strategy games 16 10.2 Research questions and methodology 18 10.2.1 The SGDL and its framework 19 10.3 The game, the agents and the assigner 21 10.3.1 Implementation details 24 10.4 Agents 24 10.4.1 Random action selection 25 10.4.2 Finite-state machine 26 10.4.3 Neuroevolution of augmenting topologies 26 v vi CONTENTS 10.4.4 MinMax 10.4.5 Monte Carlo Tree Search 10.4.6 Potential fields 10.4.7 Classifier systems 10.5 Results of agent versus agent testing 10.6 Results of human play testing 10.7 Discussion 10.8 Conclusions References LIST OF FIGURES 10.1 A typical screenshot from our example game "RockWars" 10.2 The Commander framework used for the agents in the study. 10.3 Client-server system used for training and experimentation. 10.4 Finite-state automata of the SemiRandom agent units 10.5 Finite-state automaton of the FSM agent's units 10.6 Action tree used to find MultiActions 10.7 Action tree illustrating the use of buildings. 10.8 Summary of agent versus agent results 10.9 Summary of human play results vii LIST OF TABLES 10.1 Unit properties for SGDL models 10.2 Agents in the study 10.3 Summary of agent versus agent results 10.4 Standard deviantions of agent versus agent results 10.5 Summary of human play results ix ACKNOWLEDGMENTS For more technical details, please refer to the first two authors' joint masters thesis [1], or Tobias Mahlmann's PhD thesis including the detailed description of the Strategy Games Description Language [2]. Both of these are available online at http://game.itu.dk/sgdl. This research was supported in part by the Danish Research Agency project "AGameComIn" (274-09-0083). xi CHAPTER 10 AI FOR GENERAL STRATEGY GAME PLAYING 1 For brevity, we will refer to computer strategy games as strategy games in the following sections. AI for General Strategy Game Playing.

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Optimal processing for gel electrophoresis images: Applying Monte Carlo Tree Search in GelApp

et al. 2016

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In biomedical research, gel band size estimation in electrophoresis analysis is a routine process. To facilitate and automate this process, numerous software have been released, notably the GelApp mobile app. However, the band detection accuracy is limited due to a band detection algorithm that cannot adapt to the variations in input images. To address this, we used the Monte Carlo Tree Search with Upper Confidence Bound (MCTS-UCB) method to efficiently search for optimal image processing pipelines for the band detection task, thereby improving the segmentation algorithm. Incorporating this into GelApp, we report a significant enhancement of gel band detection accuracy by 55.9 ± 2.0% for protein polyacrylamide gels, and 35.9 ± 2.5% for DNA SYBR green agarose gels. This implementation is a proof-of-concept in demonstrating MCTS-UCB as a strategy to optimize general image segmentation. The improved version of GelApp-GelApp 2.0-is freely available on both Google Play Store (for Android platform), and Apple App Store (for iOS platform).

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The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments

Cited by 85 publications

References 25 publications

Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

Optimization and Design of College Students’ Innovation and Entrepreneurship System Based on Computational Intelligence

AI for General Strategy Game Playing

Optimal processing for gel electrophoresis images: Applying Monte Carlo Tree Search in GelApp

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