Partially Observable Markov Decision Process models (POMDPs) have been applied to low-level robot control. We show how to use POMDPs differently, namely for sensorplanning in the ...
Both human and automated tutors must infer what a student knows and plan future actions to maximize learning. Though substantial research has been done on tracking and modeling stu...
Anna N. Rafferty, Emma Brunskill, Thomas L. Griffi...
We consider sensor scheduling as the optimal observability problem for partially observable Markov decision processes (POMDP). This model fits to the cases where a Markov process ...
With an eye towards design of human-in-the-loop systems, we investigate human decision making in a social context for tasks that require the human to make repeated choices among fi...
The coordination problem in multi-agent systems is the problem of managing dependencies between the activities of autonomous agents, in conditions of incomplete knowledge about th...