Bayesian modeling of human concept learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Imitation of Human Behavior in Interactive Computer Games
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Supporting wilderness search and rescue using a camera-equipped mini UAV: Research Articles
Journal of Field Robotics - Special Issue on Search and Rescue Robots
UAV intelligent path planning for wilderness search and rescue
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A cognitive model of spatial path-planning
Computational & Mathematical Organization Theory
The best papers from BRIMS 2011: models of users and teams interacting
Computational & Mathematical Organization Theory
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In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely location of the missing person. This map is important because it guides the IC in allocating search resources and coordinating efforts, but it often depends almost exclusively on the missing person profile, prior experience, and subjective judgment. We propose a Bayesian model that uses publicly available terrain features data to help model lost-person behaviors. This approach enables domain experts to encode uncertainty in their prior estimations and also makes it possible to incorporate human behavior data collected in the form of posterior distributions, which are used to build a first-order Markov transition matrix for generating a temporal, posterior predictive probability distribution map. The map can work as a base to be augmented by search and rescue workers to incorporate additional information. Using a Bayesian 驴 2 test for goodness-of-fit, we show that the model fits a synthetic dataset well. This model also serves as a foundation for a larger framework that allows for easy expansion to incorporate additional factors such as season and weather conditions that affect the lost-person's behaviors.