Creating Brain-Like Intelligence

This volume features contributions based on the presentations given at the International Symposium Creating Brain-Like Intelligence, held in Hohenstein, Germany, February 2007. It represents the current state of the art in several research fields.

TheInternationalSymposiumCreatingBrain-LikeIntelligencewasheldinFeb- ary 2007 in Germany. The symposium brought together notable scientists from di?erent 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 and arti?cial intelligence (a ?eld that had just celebrated its 50 year anniversary) and fuzzy systems, we are still not able to mimic even the lower-level sensory capabilities of humans or animals. We asked what the biggest obstacles are and how we could gain ground toward a scienti?c understanding of the autonomy, ?exibility, and robustness of intelligent biological systems as they strive to survive. New principles are usually found at the interfaces between existing disciplines, and traditional boundaries between disciplines have to be broken down to see how complex systems become simple and how the puzzle can be assembled. During the symposium we could identify some recurring themes that p- vaded many of the talks and discussions. The triad of structure, dynamics and environment,theroleoftheenvironmentasanactivepartnerinshapingsystems, adaptivity on all scales (learning, development, evolution) and the amalga- tion of an internal and external world in brain-like intelligence rate high among them. Each of us is rooted in a certain community which we have to serve with the results of our research. Looking beyond our ?elds and working at the interfaces between established areas of research requires e?ort and an active process.

Klappentext

This state-of-the-art-survey documents the scientific outcome of the International Symposium "Creating Brain-Like Intelligence", which took place in Hohenstein, Germany, in February 2007. It presents an introduction to this emerging interdisciplinary field by drawing together 15 articles from researchers across a broad range of disciplines. Brain-Like intelligence attempts a representation of the environment including the system itself. It has to cope with a continuous influx of an immense amount of mostly unspecific information and cannot be identified with a singular functionality. It is the versatility of brain-like intelligence, its robustness and plasticity which makes it the object of our quest. After 50 years of artificial intelligence research however, we are still not able to mimic even the lower level sensory capabilities of animals. But we are beginning to move in the right direction by identifying the biggest obstacles and starting to understand the autonomy, flexibility, and robustness of intelligent biological systems. This collection of articles is evidence of this progress and represents the current state of art in several research fields that are embraced by brain-like intelligence.

Inhalt
Creating Brain-Like Intelligence.- From Complex Networks to Intelligent Systems.- Stochastic Dynamics in the Brain and Probabilistic Decision-Making.- Formal Tools for the Analysis of Brain-Like Structures and Dynamics.- Morphological Computation Connecting Brain, Body, and Environment.- Trying to Grasp a Sketch of a Brain for Grasping.- Learning Actions through Imitation and Exploration: Towards Humanoid Robots That Learn from Humans.- Towards Learning by Interacting.- Planning and Moving in Dynamic Environments.- Towards Cognitive Robotics.- Approaches and Challenges for Cognitive Vision Systems.- Some Requirements for Human-Like Robots: Why the Recent Over-Emphasis on Embodiment Has Held Up Progress.- Co-evolution of Rewards and Meta-parameters in Embodied Evolution.- Active Vision for Goal-Oriented Humanoid Robot Walking.- Cognitive Adequacy in Brain-Like Intelligence.- Basal Ganglia Models for Autonomous Behavior Learning.