Titlebook: Creating Brain-Like Intelligence; From Basic Principle Bernhard Sendhoff,Edgar K?rner,Kenji Doya Book 2009 Springer-Verlag Berlin Heidelber

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From Complex Networks to Intelligent Systems,itative framework, the information processing capability of the brain depends in part on the embodied interactions of an autonomous system in an environment. Thus, the conceptual framework of complex networks might provide a basis for the understanding and design of future intelligent systems.

gruelling

Active Vision for Goal-Oriented Humanoid Robot Walking,edal walking robots are exposed to largely perturbed visual input caused by their own walking dynamics. We show that evolved robots are capable of coping with the dynamics and of accomplishing the task by means of active, efficient camera control.

編輯才信任

Absenteeism

https://doi.org/10.1007/978-3-540-77571-3ation phase. After learning, actions are selected based on probabilistic inference in the learned Bayesian network. We present results demonstrating that a 25-degree-of-freedom humanoid robot can learn dynamically stable, full-body imitative motions simply by observing a human demonstrator.

字謎游戲

Learning Actions through Imitation and Exploration: Towards Humanoid Robots That Learn from Humans,ation phase. After learning, actions are selected based on probabilistic inference in the learned Bayesian network. We present results demonstrating that a 25-degree-of-freedom humanoid robot can learn dynamically stable, full-body imitative motions simply by observing a human demonstrator.

CRAFT

Book 2009ent backgrounds and with di?erent expertise related to the emerging ?eld of brain-like intelligence. Our understanding of the principles behind brain-like intelligence is still limited. After all, we have had to acknowledge that after tremendous advances in areas like neural networks, computational

偏離

Mhc-Molecule

Crumple

獨(dú)輪車

,Do’s and Don’ts of Mine Closure,tic settling into attractor states. This can account for probabilistic decision-making, which we show can be advantageous. Similar stochastical dynamics contributes to multistable states such as pattern rivalry and binocular rivalry. Stochastical dynamics also contributes to the detectability of sig