Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
On the complexity of partially observed Markov decision processes
Theoretical Computer Science - Special issue on complexity theory and the theory of algorithms as developed in the CIS
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Isotropic sequence order learning
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning viewpoint invariant object representations using a temporal coherence principle
Biological Cybernetics
Iterative Kernel Principal Component Analysis for Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Modeling of an Internal Clock
Neural Computation
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics (Springer Tracts in Advanced Robotics)
Sparse spectrotemporal coding of sounds
EURASIP Journal on Applied Signal Processing
To Afford or Not to Afford: A New Formalization of Affordances Toward Affordance-Based Robot Control
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Chained learning architectures in a simple closed-loop behavioural context
Biological Cybernetics
Discriminative Locality Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Component Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Fast Haar transform based feature extraction for face representation and recognition
IEEE Transactions on Information Forensics and Security
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Learning Object Affordances: From Sensory--Motor Coordination to Imitation
IEEE Transactions on Robotics
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Global convergence of Oja's subspace algorithm for principal component extraction
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Modulated Hebb-Oja learning Rule-a method for principal subspace analysis
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
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For an animal to survive it has to excel in a twofold task: It has to perceive the world and execute adequate actions. These skills are acquired and adapted through perceptual and behavioral learning, respectively. Perceptual and behavioral learning are tightly interwoven, choosing the adequate action is only possible in the presence off accurate perceptions. Learning to perceive accurately does, however, happen while acting in the world. The nature of this interaction is not well understood as theoretical work does mostly investigate the two forms of learning separately. To overcome this limitation we combine perceptual and behavioral learning in a subspace learning algorithm. In a formal analysis and in numerical simulations we show that the proposed subspace learning algorithm allows us to integrate both learning systems and to smoothly change form perceptual learning only to behavioral learning only. Further we show that in a robot open area foraging task an active adaptation of the balance between perceptual and behavioral learning is necessary in order to stabilize the performance of the robot. This alludes to a fundamental argument for the necessity of a task dependent modulation of perceptual and behavioral learning as found in biological systems.