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94
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AAAI
2012
12 years 12 months ago
Kernel-Based Reinforcement Learning on Representative States
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
Branislav Kveton, Georgios Theocharous
MKWI
2008
112views Business» more  MKWI 2008»
14 years 11 months ago
A Framework for Querying in Business Process Modelling
: In order to respond quickly to changing market requirements, a business organisation needs to increase the level of agility in all phases of the business process engineering chai...
Ivan Markovic, Alessandro Costa Pereira, Nenad Sto...
ECAI
2006
Springer
15 years 1 months ago
Decision with Uncertainties, Feasibilities, and Utilities: Towards a Unified Algebraic Framework
Several formalisms exist to express and solve decision problems. Each is designed to capture different kinds of knowledge: utilities expressing preferences, uncertainties on the en...
Cédric Pralet, Gérard Verfaillie, Th...
75
Voted
ICRA
2007
IEEE
126views Robotics» more  ICRA 2007»
15 years 3 months ago
A formal framework for robot learning and control under model uncertainty
— While the Partially Observable Markov Decision Process (POMDP) provides a formal framework for the problem of robot control under uncertainty, it typically assumes a known and ...
Robin Jaulmes, Joelle Pineau, Doina Precup
RSS
2007
136views Robotics» more  RSS 2007»
14 years 11 months ago
The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty
— We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a ...
Ron Alterovitz, Thierry Siméon, Kenneth Y. ...