Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Computational principles of mobile robotics
Computational principles of mobile robotics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Imitation in animals and artifacts
Imitation in animals and artifacts
Imitation: a means to enhance learning of a synthetic protolanguage in autonomous robots
Imitation in animals and artifacts
Challenges in building robots that imitate people
Imitation in animals and artifacts
Imitation in animals and artifacts
GESwarm: grammatical evolution for the automatic synthesis of collective behaviors in swarm robotics
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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In biology/psychology, the capability of natural organisms to learn from the observation/interaction with conspecifics is referred to as social learning. Roboticists have recently developed an interest in social learning, since it might represent an effective strategy to enhance the adaptivity of a team of autonomous robots. In this study, we show that a methodological approach based on artifcial neural networks shaped by evolutionary computation techniques can be successfully employed to synthesise the individual and social learning mechanisms for robots required to learn a desired action (i.e. phototaxis or antiphototaxis).