TY - GEN
T1 - Generating diverse behaviors of evolutionary robots with speciation for theory of mind
AU - Yi, Si Hyuk
AU - Cho, Sung Bae
PY - 2012
Y1 - 2012
N2 - Theory of Mind (ToM) is the ability to read another person's mind. To apply ToM in robots, robot should read the intention from target. However, it is difficult to read target's intention directly. Robot uses the sensors to measure distance from target because distance is the feature to read target's intention. Neural network has been widely used to control the robot for generating a diverse speciation. It has been less explored in behavior-based robotics. Speciation usually relies on a distance measure that allows different from the robot to target to be compared. In this paper, we proposed novel measure to generate diverse behaviors of a robot with speciation for ToM. It includes some distance measure such as Euclidean distance, cosine distance, arctangent distance, and edit distance. It generates diverse behaviors of the robot by neural network for ToM. The proposed method has been experimented on a real e-puck robot platform.
AB - Theory of Mind (ToM) is the ability to read another person's mind. To apply ToM in robots, robot should read the intention from target. However, it is difficult to read target's intention directly. Robot uses the sensors to measure distance from target because distance is the feature to read target's intention. Neural network has been widely used to control the robot for generating a diverse speciation. It has been less explored in behavior-based robotics. Speciation usually relies on a distance measure that allows different from the robot to target to be compared. In this paper, we proposed novel measure to generate diverse behaviors of a robot with speciation for ToM. It includes some distance measure such as Euclidean distance, cosine distance, arctangent distance, and edit distance. It generates diverse behaviors of the robot by neural network for ToM. The proposed method has been experimented on a real e-puck robot platform.
UR - http://www.scopus.com/inward/record.url?scp=84871363470&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871363470&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-34859-4_49
DO - 10.1007/978-3-642-34859-4_49
M3 - Conference contribution
AN - SCOPUS:84871363470
SN - 9783642348587
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 491
EP - 500
BT - Simulated Evolution and Learning - 9th International Conference, SEAL 2012, Proceedings
T2 - 9th International Conference on Simulated Evolution and Learning, SEAL 2012
Y2 - 16 December 2012 through 19 December 2012
ER -