Achieving Efficient and Cognitively Plausible Learning in Backgammon
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Sequence Learning in the ACT-R Cognitive Architecture: Empirical Analysis of a Hybrid Model
Sequence Learning - Paradigms, Algorithms, and Applications
Journal of Field Robotics - Special Issue on Teamwork in Field Robotics
How search and its subtasks scale in N robots
Proceedings of the 4th ACM/IEEE international conference on Human robot interaction
Multimodal cognitive architecture: making perception more central to intelligent behavior
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Computational & Mathematical Organization Theory
An integrated model of eye movements and visual encoding
Cognitive Systems Research
The acquisition of spatial navigational skills from dynamic versus static visualisations
BCS-HCI '12 Proceedings of the 26th Annual BCS Interaction Specialist Group Conference on People and Computers
The best papers from BRIMS 2011: models of users and teams interacting
Computational & Mathematical Organization Theory
The cognitive benefits of dynamic representations in the acquisition of spatial navigation skills
Computers in Human Behavior
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Planning a path to a destination, given a number of options and obstacles, is a common task. We suggest a two-component cognitive model that combines retrieval of knowledge about the environment with search guided by visual perception. In the first component, subsymbolic information, acquired during navigation, aids in the retrieval of declarative information representing possible paths to take. In the second component, visual information directs the search, which in turn creates knowledge for the first component. The model is implemented using the ACT-R cognitive architecture and makes realistic assumptions about memory access and shifts in visual attention. We present simulation results for memory-based high-level navigation in grid and tree structures, and visual navigation in mazes, varying relevant cognitive (retrieval noise and visual finsts) and environmental (maze and path size) parameters. The visual component is evaluated with data from a multi-robot control experiment, where subjects planned paths for robots to explore a building. We describe a method to compare trajectories without referring to aligned points in the itinerary. The evaluation shows that the model provides a good fit, but also that planning strategies may vary with task loads.