INVESTIGADORES
ZANUTTO Bonifacio Silvano
congresos y reuniones científicas
Título:
Multiagent team formation performed by operant learning: an animat approach
Autor/es:
D. GUTNISKY, R. ZELMANN, S. ZANUTTO
Lugar:
Vancover
Reunión:
Congreso; IJCNN 06; 2006
Institución organizadora:
IEEE
Resumen:
Abstract An animat approach to dynamic team formation in a
group of distributed robots is studied. The goal is that robots
learn to align with the others in order to form a row or a column
without having communication among them, just local sensing
and a reinforcement signal. The action of the robot is controlled
by a biologically plausible neural network model of operant
learning. The remarkable performance achieved by the
proposed model allows the building of new Artificial Intelligence
agents based on neurobiology, psychology and ethology
research. An animat approach to dynamic team formation in a
group of distributed robots is studied. The goal is that robots
learn to align with the others in order to form a row or a column
without having communication among them, just local sensing
and a reinforcement signal. The action of the robot is controlled
by a biologically plausible neural network model of operant
learning. The remarkable performance achieved by the
proposed model allows the building of new Artificial Intelligence
agents based on neurobiology, psychology and ethology
research.