Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Intelligence without representation
Artificial Intelligence
Integrating perception, action and learning
ACM SIGART Bulletin
Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
A cognitive architecture for artificial vision
Artificial Intelligence
Improving the DAC architecture by using proprioceptive sensors
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Layered control architectures in robots and vertebrates
Adaptive Behavior
An Behavior-based Robotics
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Computer and Robot Vision
AFPAC '00 Proceedings of the Second International Workshop on Algebraic Frames for the Perception-Action Cycle
KI '95 Proceedings of the 19th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Channel Smoothing: Efficient Robust Smoothing of Low-Level Signal Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autonomous Learning of Object Appearances using Colour Contour Frames
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Geometric Hashing with Local Affine Frames
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
An Adaptive Classification Algorithm Using Robust Incremental Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Correspondence-free Associative Learning
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Fusion Algorithm for Locally Arranged Linear Models
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Cognitive vision: The case for embodied perception
Image and Vision Computing
Efficient computation of channel-coded feature maps through piecewise polynomials
Image and Vision Computing
Problem solving through imitation
Image and Vision Computing
Accurate interpolation in appearance-based pose estimation
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Visual bootstrapping for unsupervised symbol grounding
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Unsupervised symbol grounding and cognitive bootstrapping in cognitive vision
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Three dilemmas of signal- and symbol-based representations in computer vision
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
IEEE Transactions on Evolutionary Computation
The application of an oblique-projected Landweber method to a model of supervised learning
Mathematical and Computer Modelling: An International Journal
When is a cognitive system embodied?
Cognitive Systems Research
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The major goal of the COSPAL project is to develop an artificial cognitive system architecture, with the ability to autonomously extend its capabilities. Exploratory learning is one strategy that allows an extension of competences as provided by the environment of the system. Whereas classical learning methods aim at best for a parametric generalization, i.e., concluding from a number of samples of a problem class to the problem class itself, exploration aims at applying acquired competences to a new problem class, and to apply generalization on a conceptual level, resulting in new models. Incremental or online learning is a crucial requirement to perform exploratory learning. In the COSPAL project, we mainly investigate reinforcement-type learning methods for exploratory learning, and in this paper we focus on the organization of cognitive systems for efficient operation. Learning is used over the entire system. It is organized in the form of four nested loops, where the outermost loop reflects the user-reinforcement-feedback loop, the intermediate two loops switch between different solution modes at symbolic respectively sub-symbolic level, and the innermost loop performs the acquired competences in terms of perception-action cycles. We present a system diagram which explains this process in more detail. We discuss the learning strategy in terms of learning scenarios provided by the user. This interaction between user ('teacher') and system is a major difference to classical robotics systems, where the system designer places his world model into the system. We believe that this is the key to extendable robust system behavior and successful interaction of humans and artificial cognitive systems. We furthermore address the issue of bootstrapping the system, and, in particular, the visual recognition module. We give some more in-depth details about our recognition method and how feedback from higher levels is implemented. The described system is however work in progress and no final results are available yet. The available preliminary results that we have achieved so far, clearly point towards a successful proof of the architecture concept.