FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

Published in

(2017)

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TL;DR

The experimental results demonstrate that the proposed FDAA can work effectively for Go applications and an FML-based Human-Machine Cooperative System for the game of Go.

Abstract

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 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 game results commentary are presented. We experimentally verify and validate the performance of the FDAA and variants of the FHMCS by testing five games in 2016 and 60 games of Google Master Go, a new version of the AlphaGo program, in January 2017. The experimental results demonstrate that the proposed FDAA can work effectively for Go applications.

Authors

Chang-Shing Lee

1 papers

Mei-Hui Wang

1 papers

Sheng-Chi Yang

1 papers

References47 items

FML-based prediction agent and its application to game of Go

Google reveals secret test of AI bot to beat top Go players

IEEE Standards [Editor's Remarks]

IEEE 1855TM: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs]

Human vs. Computer Go: Review and Prospect [Discussion Forum]

FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go

Published in

(2017)

External Links:

Generate Graph DownloadPDF

TL;DR

The experimental results demonstrate that the proposed FDAA can work effectively for Go applications and an FML-based Human-Machine Cooperative System for the game of Go.

Abstract

Authors

Chang-Shing Lee

1 papers

Mei-Hui Wang

1 papers

Sheng-Chi Yang

1 papers

References47 items

FML-based prediction agent and its application to game of Go

Google reveals secret test of AI bot to beat top Go players

IEEE Standards [Editor's Remarks]

IEEE 1855TM: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs]

Human vs. Computer Go: Review and Prospect [Discussion Forum]

Pi-Hsia Hung

1 papers

Chun-Hsun Chou

1 papers

Ping-Chiang Chou

1 papers

Chia-Hsiu Kao

1 papers

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Better Computer Go Player with Neural Network and Long-term Prediction

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DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition

T2FS-Based Adaptive Linguistic Assessment System for Semantic Analysis and Human Performance Evaluation on Game of Go

Move Evaluation in Go Using Deep Convolutional Neural Networks

Teaching Deep Convolutional Neural Networks to Play Go

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Soft-computing-based emotional expression mechanism for game of computer Go

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A Survey of Monte Carlo Tree Search Methods

Monte-Carlo tree search and rapid action value estimation in computer Go

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Creating personalized assessments based on learner knowledge and objectives in a hypermedia Web testing application

Special Issue on Monte Carlo Techniques and Computer Go

Fuego—An Open-Source Framework for Board Games and Go Engine Based on Monte Carlo Tree Search

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Evolutionary swarm neural network game engine for Capture Go

Zone of Proximal Development

Foundations for a New Science of Learning

Monte-Carlo simulation balancing

Applications of neural networks in Computer Go

Investigating the learning transfer of genre features and conceptual knowledge from an academic literacy course to business studies: Exploring the potential of dynamic assessment

TL;DR

Abstract

Authors

References47 items

FML-based prediction agent and its application to game of Go

Google reveals secret test of AI bot to beat top Go players

IEEE Standards [Editor's Remarks]

IEEE 1855TM: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs]

Human vs. Computer Go: Review and Prospect [Discussion Forum]

TL;DR

Abstract

Authors

References47 items

FML-based prediction agent and its application to game of Go

Google reveals secret test of AI bot to beat top Go players

IEEE Standards [Editor's Remarks]

IEEE 1855TM: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs]

Human vs. Computer Go: Review and Prospect [Discussion Forum]

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images

Transferred Deep Convolutional Neural Network Features for Extensive Facial Landmark Localization

Mastering the game of Go with deep neural networks and tree search

Dog DNA probed for clues to human psychiatric ills

Better Computer Go Player with Neural Network and Long-term Prediction

Adaptive Personalized Diet Linguistic Recommendation Mechanism Based on Type-2 Fuzzy Sets and Genetic Fuzzy Markup Language

DropSample: A New Training Method to Enhance Deep Convolutional Neural Networks for Large-Scale Unconstrained Handwritten Chinese Character Recognition

T2FS-Based Adaptive Linguistic Assessment System for Semantic Analysis and Human Performance Evaluation on Game of Go

Move Evaluation in Go Using Deep Convolutional Neural Networks

Teaching Deep Convolutional Neural Networks to Play Go

Putting big data to good use in neuroscience

On the Power of Fuzzy Markup Language

Soft-computing-based emotional expression mechanism for game of computer Go

A NOVEL GENETIC FUZZY MARKUP LANGUAGE AND ITS APPLICATION TO HEALTHY DIET ASSESSMENT

A Survey of Monte Carlo Tree Search Methods

Monte-Carlo tree search and rapid action value estimation in computer Go

Current Frontiers in Computer Go

Creating personalized assessments based on learner knowledge and objectives in a hypermedia Web testing application

Special Issue on Monte Carlo Techniques and Computer Go

Fuego—An Open-Source Framework for Board Games and Go Engine Based on Monte Carlo Tree Search

Item Response Theory

Evolutionary swarm neural network game engine for Capture Go

Zone of Proximal Development

Foundations for a New Science of Learning

Monte-Carlo simulation balancing

Applications of neural networks in Computer Go

Investigating the learning transfer of genre features and conceptual knowledge from an academic literacy course to business studies: Exploring the potential of dynamic assessment

Exploring the potential of dynamic assessment, Journal of English for Academic Purposes

Google secretly tested AI bot: updated version of Google DeepMind’s AlphaGo program revealed as mystery online player

review and prospect, IEEE Computational Intelligence Magazine

American Speech-Language Hearing Association, Dynamic assessment: basic framework

Go game

IEEE Computational Intelligence Society, 1855-2016-IEEE Standard for Fuzzy Markup Language

Fujisoft in Japan

Google masters Go: deep-learning software excels at complex ancient board game

Fuzzy Markup Language: A XML Based Language for Enabling Full Interoperability in Fuzzy Systems Design

Distributing Fuzzy Reasoning through Fuzzy Markup Language: An Application to Ambient Intelligence

Accepted for publication in International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems in July, 2017 engine for Capture Go

Gradient-based learning applied to document recognition

IEEE standards

Google Deep Mind AlphaGo at The Future of Go Summit, 23-27 May 2017

swarm neural network game

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