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» Model Minimization in Markov Decision Processes
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FLAIRS
2004
13 years 10 months ago
State Space Reduction For Hierarchical Reinforcement Learning
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...
Mehran Asadi, Manfred Huber
INFOCOM
2011
IEEE
13 years 4 days ago
A high-throughput routing metric for reliable multicast in multi-rate wireless mesh networks
Abstract—We propose a routing metric for enabling highthroughput reliable multicast in multi-rate wireless mesh networks. This new multicast routing metric, called expected multi...
Xin Zhao, Jun Guo, Chun Tung Chou, Archan Misra, S...
AIED
2011
Springer
13 years 10 days ago
Faster Teaching by POMDP Planning
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...
PERCOM
2007
ACM
14 years 8 months ago
Sensor Scheduling for Optimal Observability Using Estimation Entropy
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 ...
Mohammad Rezaeian
CDC
2010
IEEE
136views Control Systems» more  CDC 2010»
13 years 3 months ago
The role of social feedback in steady-state performance of human decision making for two-alternative choice tasks
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...
Andrew Reed Stewart, Naomi Ehrich Leonard