Models for sequential decision making under uncertainty (e.g., Markov decision processes,or MDPs) have beenstudied in operations research for decades. The recent incorporation of ...
Markov decision processes (MDPs) and contingency planning (CP) are two widely used approaches to planning under uncertainty. MDPs are attractive because the model is extremely gen...
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 ...
Spoken dialogue managers have benefited from using stochastic planners such as Markov Decision Processes (MDPs). However, so far, MDPs do not handle well noisy and ambiguous speec...
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...