Automation of the Arabic Sign Language Recognition using the Power Glove
 

M. Mohandes, S. Buraiky

Electrical Engineering Dept. King  Fahd University of Petroleum and Minerals, KFUPM 1885, Dhahran, 31261, Saudi Arabia

Abstract:

Reinforcement learning algorithm based-on solving RTDP does not have to evaluate the entire state space in order to deliver an optimal policy, and yet it can often obtain good policy. The DP always is employed to be a learning Agent's environment. In this paper, we propose a novel algorithm called V-RTDP to solve Agent learning problems with full observability. Here, we use a new DP framework in varying environment instead of a general one. Our experiment demonstrates that the algorithm is very efficient.

Keywords: Real-time Dynamic Programming, Variational Environment, Reinforcement Learning, Algorithm, Optimal Policy

(Full Paper, 904 KB)

Biographies:

Mohamed Mohandes is an associate professor at King Fahd University of Petroleum and Minerals. He obtained MS from University of Missouri-Columbia in 1989 and PhD from Purdue University in 1993, both in Electrical and Computer Engineering. His area of research includes: Sign Language recognition, Neural networks, and intelligent systems.

BibTex:

@ARTICLE{P1120714001,

AUTHOR = {M. Mohandes and S. A-Buraiky},

TITLE = {Automation of the Arabic Sign Language Recognition using the Power Glove},

JOURNAL =  {ICGST  International Journal on Artificial Intelligence and Machine Learning, AIML},

YEAR = {2007},

VOLUME = {7},

ISSUE ={1} ,

PAGES={41--46}

}

(Full Paper, 904 KB)