Navigating with a rat brain: a neurobiologically-inspired model for robot spatial representation
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Active vision
Dynamic action sequences in reinforcement learning
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
An Behavior-based Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Efficient Exploration In Reinforcement Learning
Efficient Exploration In Reinforcement Learning
Eye Movements in Visual Cognition: A Computational Study
Eye Movements in Visual Cognition: A Computational Study
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Spatial learning for navigation in dynamic environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Perhaps the most striking feature of the human problem solving ability is its apparent generality. Many animals have well developed path finding and navigational abilities and this suggests a route by which general purpose problem solving skills might have arisen in primates. The idea explored in this paper is that by generalising the notion of 'place' to include contexts defined by factors other than just spatial location a powerful general purpose representational and planning mechanism can be built on the foundations provided by existing models of animal navigation. In the model presented the animat is assumed to have available to it motor primitives that not only allow it to move within and modify its environment but also allow it to change the way in which its sensory system operates, e.g. change its field of view. By selectively directing its attention in this way the animat is able to build up a dynamic multi-scale representation of its environment.