Playing Tic-Tac-Toe Using Genetic Neural Network with Double Transfer Functions - Journal of Intelligent Learning Systems and Applications

JILSA > Vol.3 No.1, February 2011

Journal of Intelligent Learning Systems and Applications

Volume 3, Issue 1 (February 2011)

ISSN Print: 2150-8402 ISSN Online: 2150-8410

Google-based Impact Factor: 1.5 Citations

Playing Tic-Tac-Toe Using Genetic Neural Network with Double Transfer Functions ()

HTML

Download as PDF (Size: 113KB) PP. 37-44

DOI: 10.4236/jilsa.2011.31005 8,910 Downloads 18,039 Views Citations

Author(s)

Sai Ho Ling, Hak Keung Lam

Affiliation(s)

ABSTRACT

Computational intelligence is a powerful tool for game development. In this paper, an algorithm of playing the game Tic-Tac-Toe with computational intelligence is developed. This algorithm is learned by a Neural Network with Double Transfer functions (NNDTF), which is trained by genetic algorithm (GA). In the NNDTF, the neuron has two transfer functions and exhibits a node-to-node relationship in the hidden layer that enhances the learning ability of the net-work. A Tic-Tac-Toe game is used to show that the NNDTF provide a better performance than the traditional neural network does.

KEYWORDS

Game, Genetic Algorithm, Neural Network

Share and Cite:

S. Ling and H. Lam, "Playing Tic-Tac-Toe Using Genetic Neural Network with Double Transfer Functions," Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 1, 2011, pp. 37-44. doi: 10.4236/jilsa.2011.31005.

Cited by

[1]	A case study on computer-aided diagnosis of nonerosive reflux disease using deep learning techniques
	2021

[2]	An Integrated Expert System with a Supervised Machine Learning based Probabilistic Approach to Play Tic-Tac-Toe
	2021 IEEE 12th Annual …, 2021

[3]	A wise up visual robot driven by a self-taught neural agent
	2020

[4]	Randomised fast no-loss expert system to play tic-tac-toe like a human
	2020

[5]	Self-taught Neural Agents in Clever Game Playing
	2020

[6]	Randomised fast no‐loss expert system to play tic‐tac‐toe like a human
	Cognitive Computation and Systems, 2020

[7]	Improved technique in Tic-Tac-Toe game to minimize the condition of draw using min-max over optimal strategy
	International Journal of Scientific & Engineering Research, 2019

[8]	Modeling of Agent Cognition in Extensive Games via Artificial Neural Networks
	2018

[9]	Board Games AI
	Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction, 2018

[10]	The winning strategy of Tic Tac Toe Game model by using Theoretical Computer Science
	2017

[11]	The winner decision model of tic tac toe game by using multi-tape turing machine
	2016

[12]	A simple algorithm for designing an artificial intelligence based Tic Tac Toe game
	Pervasive Computing (ICPC), 2015 International Conference on, 2015

[13]	종료된 삼목게임의 전략 적합도 인식을 위한 진화신경망 모델
	Journal of The Korean Society for Computer Game, 2015

[14]	A" Never-Loose" Strategy to Play the Game of Tic-Tac-Toe
	Soft Computing and Machine Intelligence (ISCMI), 2014 International Conference on, 2014

[15]	유전 알고리즘을 이용한 삼목 게임 전략 분석
	한국게임학회 논문지, 2014

[16]	Analysis of Tic-Tac-Toe Game Strategies using Genetic Algorithm
	2014

[17]	Genetic Optimization of Artificial Neural Networks to Forecast Virioplankton Abundance from Cytometric Data
	Journal of Intelligent Learning Systems and Applications, 2013

[18]	A Simple Algorithm For Desig Intelligence Based Tic Ta
	2012

[19]	Genetic Algorithms for Perceptual Codes Extraction
	Journal of Intelligent Learning Systems and Applications, 2012

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journals Menu

Home

About SCIRP

Service

Policies