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DA
2010
139views more  DA 2010»
13 years 2 months ago
Eliciting Patients' Revealed Preferences: An Inverse Markov Decision Process Approach
. Direct approaches, which involve asking patients various abstract questions, have significant drawbacks. We propose a new approach that infers patient preferences based on observ...
Zeynep Erkin, Matthew D. Bailey, Lisa M. Maillart,...
ECAI
2008
Springer
13 years 6 months ago
A Simulation-based Approach for Solving Generalized Semi-Markov Decision Processes
Time is a crucial variable in planning and often requires special attention since it introduces a specific structure along with additional complexity, especially in the case of dec...
Emmanuel Rachelson, Gauthier Quesnel, Fréd&...
AIPS
2011
12 years 8 months ago
Sample-Based Planning for Continuous Action Markov Decision Processes
In this paper, we present a new algorithm that integrates recent advances in solving continuous bandit problems with sample-based rollout methods for planning in Markov Decision P...
Christopher R. Mansley, Ari Weinstein, Michael L. ...
ECAI
2004
Springer
13 years 10 months ago
On-Line Search for Solving Markov Decision Processes via Heuristic Sampling
In the past, Markov Decision Processes (MDPs) have become a standard for solving problems of sequential decision under uncertainty. The usual request in this framework is the compu...
Laurent Péret, Frédérick Garc...
JMLR
2008
159views more  JMLR 2008»
13 years 5 months ago
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to ...
Andreas Krause, Ajit Paul Singh, Carlos Guestrin