A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
Artificial Intelligence
Investigation of a sensorimotor system for saccadic scene analysis: an integrated approach
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Decision Support Systems with Adaptive Reasoning Strategies
Foundations of Computer Science: Potential - Theory - Cognition, to Wilfried Brauer on the occasion of his sixtieth birthday
A new view of the primary visual cortex
Neural Networks - 2004 Special issue Vision and brain
| Hi-index | 0.00 |
Humans are very efficient in the analysis, exploration and representation of their environment. Based on the neurobiological and cognitive principles of human information processing, we develop a system for the automatic identification and exploration of spatial configurations. The system sequentially selects "informative" regions (regions of interest), identifies the local structure, and uses this information for drawing efficient conclusions about the current scene. The selection process involves low-level, bottom-up processes for sensory feature extraction, and cognitive top-down processes for the generation of active motor commands that control the positioning of the sensors towards the most informative regions. Both processing levels have to deal with uncertain data, and have to take into account previous knowledge from statistical properties and learning. We suggest that this can be achieved in a hybrid architecture which integrates a nonlinear filtering stage modelled after the neural computations performed in the early stages of the visual system, and a cognitive reasoning strategy that operates in an adaptive fashion on a belief distribution.