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FLAIRS
2001
14 years 9 days ago
Practical Modeling of Bayesian Decision Problems -- Exploiting Deterministic Relations
Thewidespreaduse of influence diagramsto represent andsolve Bayesiandecision problemsis still limited by the inflexibility andrather restrictive semanticsof influence diagrams. In...
Anders L. Madsen, Kristian G. Olesen, Søren...
WSC
1998
14 years 7 days ago
Adaptive Stochastic Manpower Scheduling
Bayesian forecasting models provide distributional estimates for random parameters, and relative to classical schemes, have the advantage that they can rapidly capture changes in ...
Elmira Popova, David P. Morton
AAAI
2000
14 years 8 days ago
Decision Making under Uncertainty: Operations Research Meets AI (Again)
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 ...
Craig Boutilier
NIPS
2008
14 years 10 days ago
Structure Learning in Human Sequential Decision-Making
We use graphical models and structure learning to explore how people learn policies in sequential decision making tasks. Studies of sequential decision-making in humans frequently...
Daniel Acuña, Paul R. Schrater
PKDD
2010
Springer
179views Data Mining» more  PKDD 2010»
13 years 8 months ago
Gaussian Processes for Sample Efficient Reinforcement Learning with RMAX-Like Exploration
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
Tobias Jung, Peter Stone