Abstract
In this paper, we describe the development of an artificial neural network strategy for an industrial robot to play the game of Tic-Tac-Toe. This project was undertaken by two high school students during the university's technology and engineering research programme. The strategy is based on the feedforward multilayered neural networks with backpropagation of error training. The performance of the strategy is evaluated by its accomplishment against human opponents. The results indicate that the neural network strategy developed will almost always win if given the opportunity and at most draw if not. The neural network strategy developed has been successfully interfaced with a Scorbot-ER VII robot via an in-house designed electronic game board.
| Original language | English |
|---|---|
| Pages (from-to) | 61-70 |
| Number of pages | 10 |
| Journal | Artificial Intelligence in Engineering |
| Volume | 10 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1996 |
| Externally published | Yes |
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Keywords
- Error training
- Neural network
- Tic-Tac-Toe
ASJC Scopus subject areas
- Computer Science(all)
- Engineering(all)
Cite this
A high school project on artificial intelligence in robotics. / Fok, S. C.; Ong, E. K.
In: Artificial Intelligence in Engineering, Vol. 10, No. 1, 1996, p. 61-70.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A high school project on artificial intelligence in robotics
AU - Fok, S. C.
AU - Ong, E. K.
PY - 1996
Y1 - 1996
N2 - In this paper, we describe the development of an artificial neural network strategy for an industrial robot to play the game of Tic-Tac-Toe. This project was undertaken by two high school students during the university's technology and engineering research programme. The strategy is based on the feedforward multilayered neural networks with backpropagation of error training. The performance of the strategy is evaluated by its accomplishment against human opponents. The results indicate that the neural network strategy developed will almost always win if given the opportunity and at most draw if not. The neural network strategy developed has been successfully interfaced with a Scorbot-ER VII robot via an in-house designed electronic game board.
AB - In this paper, we describe the development of an artificial neural network strategy for an industrial robot to play the game of Tic-Tac-Toe. This project was undertaken by two high school students during the university's technology and engineering research programme. The strategy is based on the feedforward multilayered neural networks with backpropagation of error training. The performance of the strategy is evaluated by its accomplishment against human opponents. The results indicate that the neural network strategy developed will almost always win if given the opportunity and at most draw if not. The neural network strategy developed has been successfully interfaced with a Scorbot-ER VII robot via an in-house designed electronic game board.
KW - Error training
KW - Neural network
KW - Tic-Tac-Toe
UR - http://www.scopus.com/inward/record.url?scp=0029751452&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029751452&partnerID=8YFLogxK
U2 - 10.1016/0954-1810(95)00016-X
DO - 10.1016/0954-1810(95)00016-X
M3 - Article
AN - SCOPUS:0029751452
VL - 10
SP - 61
EP - 70
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
SN - 1474-0346
IS - 1
ER -