UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 7 Issue 4
April-2020
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2004502


Registration ID:
231334

Page Number

1-11

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Title

Chain Reaction Artificial Intelligence Model

Abstract

As many of us play Zero Sum Board games like Chess,tic-tac-toe,Chain Reaction etc,so we note that between Human and AI,Generally Human wins over AI.The reason is because of the dumb and lame strategies or moves played by the AI of the game because of wrong decision making.So we believe that Board games are amazing examples of decision making under uncertainty. In specific, a few games have such an immense state space and high level of uncertainty that conventional algorithms and strategies struggle to play them effectively.The aim of this paper is to put forward Monte-Carlo Tree Search Algorithm as a unified framework to a Board game's AI. In the modeled framework, randomized explorations of the search space are utilized to anticipate the most promising game moves.The approach allows to exploit the strengths and overcome the weaknesses of the given AI and facilitates the creation of a superior AI in a service-oriented fashion.All these AIs aim to select the best possible move in a given situation, i.e. for a given board configuration by predicting the next moves of the human player and creating its hashmap. In Chain Reaction the most vital part of the game is to find the best position to make a corresponding move on the board at that position. Hence, Our artificial intelligence model for the game of a board game would first need to identify the best position, predict the opponent’s future moves on the basis of previous moves select the optimal move and then make the move at that location.Particularly, we want to explore the scope of Monte Carlo Tree Search in Zero Sum Board Games like Chess,Chain Reaction,Tic-Tac Toe.We have used Chain Reaction as a basis for our Research. Based on the concepts of Model Based on the concepts of Monte Carlo Tree Search, a conceptual game model is developed in a Live Access Server extension of Visual Studio Code i.e a web browser extension to live reload and designed to provide access to the uploaded HTML/JavaScript/CSS/ ES6 files where Monte Carlo Tree Search Algorithm has been implemented for the game’s Artificial Intelligence.This research concludes a proof of practical concept that is to believed to be very useful for Zero-Sum Board Games.

Key Words

Zero sum Board games, Chain Reaction, Artificial Intelligence, Monte Carlo Tree Search.

Cite This Article

"Chain Reaction Artificial Intelligence Model ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.1-11, April 2020, Available :http://www.jetir.org/papers/JETIR2004502.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Chain Reaction Artificial Intelligence Model ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp1-11, April 2020, Available at : http://www.jetir.org/papers/JETIR2004502.pdf

Publication Details

Published Paper ID: JETIR2004502
Registration ID: 231334
Published In: Volume 7 | Issue 4 | Year April-2020
DOI (Digital Object Identifier):
Page No: 1-11
Country: Faridabad, Haryana, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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