A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
Dempster's rule of combination is #P-complete (research note)
Artificial Intelligence
Artificial Intelligence
Uncertainty measures for evidential reasoning. II: A new measure of total uncertainty
International Journal of Approximate Reasoning
The spatial semantic hierarchy
Artificial Intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Self-Organizing Maps
Decision Support Systems with Adaptive Reasoning Strategies
Foundations of Computer Science: Potential - Theory - Cognition, to Wilfried Brauer on the occasion of his sixtieth birthday
Learning Occupancy Grid Maps with Forward Sensor Models
Autonomous Robots
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
Navigation and Acquisition of Spatial Knowledge in a Virtual Maze
Journal of Cognitive Neuroscience
Efficient Wayfinding in Hierarchically Regionalized Spatial Environments
Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space
Hierarchical clustering of sensorimotor features
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Particle filtering in the Dempster--Shafer theory
International Journal of Approximate Reasoning
Target identification based on the transferable belief model interpretation of dempster-shafer model
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Particle filtering in the Dempster--Shafer theory
International Journal of Approximate Reasoning
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Determining one's position within the environment is a basic feature of spatial behavior and spatial cognition. This task is of inherently sensorimotor nature in that it results from a combination of sensory features and motor actions, where the latter comprise exploratory movements to different positions in the environment. Biological agents achieve this in a robust and effortless fashion, which prompted us to investigate a bio-inspired architecture to study the localization process of an artificial agent which operates in virtual spatial environments. The spatial representation in this architecture is based on sensorimotor features that comprise sensory sensory features as well as motor actions. It is hierarchically organized and its structure can be learned in an unsupervised fashion by an appropriate clustering rule. In addition, the architecture has a temporal belief update mechanism which explicitly utilizes the statistical correlations of actions and locations. The architecture is hybrid in integrating bottom-up processing of sensorimotor features with topdown reasoning which is able to select optimal motor actions based on the principle of maximum information gain. The architecture operates on two sensorimotor levels, a macro-level, which controls the movements of the agent in space, and on a micro-level, which controls its eye movements. As a result, the virtual mobile agent is able to localize itself within an environment using a minimum number of exploratory actions.