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ML
2002
ACM
143views Machine Learning» more  ML 2002»
14 years 9 months ago
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
An issue that is critical for the application of Markov decision processes MDPs to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas...
Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
MFCS
2004
Springer
15 years 3 months ago
Complexity of Decision Problems for Simple Regular Expressions
We study the complexity of the inclusion, equivalence, and intersection problem for simple regular expressions arising in practical XML schemas. These basically consist of the conc...
Wim Martens, Frank Neven, Thomas Schwentick
AIPS
1998
14 years 11 months ago
Solving Stochastic Planning Problems with Large State and Action Spaces
Planning methods for deterministic planning problems traditionally exploit factored representations to encode the dynamics of problems in terms of a set of parameters, e.g., the l...
Thomas Dean, Robert Givan, Kee-Eung Kim
71
Voted
AIPS
2003
14 years 11 months ago
Recommendation as a Stochastic Sequential Decision Problem
Recommender systems — systems that suggest to users in e-commerce sites items that might interest them — adopt a static view of the recommendation process and treat it as a pr...
Ronen I. Brafman, David Heckerman, Guy Shani
ECML
2005
Springer
15 years 3 months ago
Machine Learning of Plan Robustness Knowledge About Instances
Abstract. Classical planning domain representations assume all the objects from one type are exactly the same. But when solving problems in the real world systems, the execution of...
Sergio Jiménez, Fernando Fernández, ...