TY - GEN
T1 - Robot learning by observation based on Bayesian networks and game pattern graphs for human-robot game interactions
AU - Lee, Hyunglae
AU - Kim, Hyoungnyoun
AU - Park, Kyung Hwa
AU - Park, Ji Hyung
PY - 2008
Y1 - 2008
N2 - This paper describes a new learning by observation algorithm based on Bayesian networks and game pattern graphs. Even with minimal knowledge of a game or human instructions, the robot can learn the game rules by watching human demonstrators repeatedly play the game multiple times. Based on the knowledge acquired from this learning process, represented in Bayesian networks and game pattern graphs, the robot can play games as robustly as humans do. Our learning algorithm for human-robot game interaction is implemented using a teddy bear-like robot and is demonstrated by application to well-known social games, specifically Rock-Paper-Scissors, Muk-Chi-ba and Blackjack.
AB - This paper describes a new learning by observation algorithm based on Bayesian networks and game pattern graphs. Even with minimal knowledge of a game or human instructions, the robot can learn the game rules by watching human demonstrators repeatedly play the game multiple times. Based on the knowledge acquired from this learning process, represented in Bayesian networks and game pattern graphs, the robot can play games as robustly as humans do. Our learning algorithm for human-robot game interaction is implemented using a teddy bear-like robot and is demonstrated by application to well-known social games, specifically Rock-Paper-Scissors, Muk-Chi-ba and Blackjack.
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U2 - 10.1109/IROS.2008.4650861
DO - 10.1109/IROS.2008.4650861
M3 - Conference contribution
AN - SCOPUS:69549121869
SN - 9781424420582
T3 - 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
SP - 319
EP - 325
BT - 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
T2 - 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
Y2 - 22 September 2008 through 26 September 2008
ER -