Overview Interdisciplinary authors explain latest theories on mammalian intelligence and learning, artificial intelligence, creativity, and evolution Identifies scientific and technological open challenges and most promising research directions. Grounds theoretical with practical robotics experiments. It has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement. To learn autonomously and in a cumulative fashion is one of the hallmarks of intelligence, and we know that higher mammals engage in exploratory activities that are not directed to pursue goals of immediate relevance for survival and reproduction but are instead driven by intrinsic motivations such as curiosity, interest in novel stimuli or surprising events, and inter

Intrinsically motivated learning in natural and artificial systems

Baldassarre Gianluca;Mirolli Marco
2013

Abstract

Overview Interdisciplinary authors explain latest theories on mammalian intelligence and learning, artificial intelligence, creativity, and evolution Identifies scientific and technological open challenges and most promising research directions. Grounds theoretical with practical robotics experiments. It has become clear to researchers in robotics and adaptive behaviour that current approaches are yielding systems with limited autonomy and capacity for self-improvement. To learn autonomously and in a cumulative fashion is one of the hallmarks of intelligence, and we know that higher mammals engage in exploratory activities that are not directed to pursue goals of immediate relevance for survival and reproduction but are instead driven by intrinsic motivations such as curiosity, interest in novel stimuli or surprising events, and inter
2013
Istituto di Scienze e Tecnologie della Cognizione - ISTC
Cognitive psychology
computational intelligence
complexity
neuroscience
artificial intelligence
artificial life
cognition
creativity
development
ecmbodiment
evolution
fun
humanoids
learning
mechatronics
motivation
neuroscience
novelty
reinforcement learning
robotics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/250278
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