Mental models: towards a cognitive science of language, inference, and consciousness
Mental models: towards a cognitive science of language, inference, and consciousness
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Language Games for Autonomous Robots
IEEE Intelligent Systems
Learning Semantic Combinatoriality from the Interaction between Linguistic and Behavioral Processes
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Mathematical Programming: Series A and B
The iCub humanoid robot: an open platform for research in embodied cognition
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
IEEE Transactions on Autonomous Mental Development
Integration of Speech and Action in Humanoid Robots: iCub Simulation Experiments
IEEE Transactions on Autonomous Mental Development
Evolution of communication and language using signals, symbols, andwords
IEEE Transactions on Evolutionary Computation
Intrinsic Motivation Systems for Autonomous Mental Development
IEEE Transactions on Evolutionary Computation
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In this paper we present a neuro-robotic model that uses artificial neural networks for investigating the relations between the development of symbol manipulation capabilities and of sensorimotor knowledge in the humanoid robot iCub. We describe a cognitive robotics model in which the linguistic input provided by the experimenter guides the autonomous organization of the robot's knowledge. In this model, sequences of linguistic inputs lead to the development of higher-order concepts grounded on basic concepts and actions. In particular, we show that higher-order symbolic representations can be indirectly grounded in action primitives directly grounded in sensorimotor experiences. The use of recurrent neural network also permits the learning of higher-order concepts based on temporal sequences of action primitives. Hence, the meaning of a higher-order concept is obtained through the combination of basic sensorimotor knowledge. We argue that such a hierarchical organization of concepts can be a possible account for the acquisition of abstract words in cognitive robots.