Learning sorting and decision trees with POMDPs

Learning sorting and decision trees with POMDPs (1998)

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by Blai Bonet , Héctor Geffner

Venue:	In Proceedings of the Fifteenth International Conference on Machine Learning
Citations:	11 - 5 self

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Datum	Value	Source
TITLE	Learning sorting and decision trees with POMDPs	SVM HeaderParse 0.1
AUTHOR NAME	Blai Bonet	user correction - Legacy Corrections
AUTHOR AFFIL	Departamento de Computaci'on; Universidad Sim'on Bol'ivar	user correction - Legacy Corrections
AUTHOR ADDR	Aptdo. 89000, Caracas 1080-A; Venezuela	user correction - Legacy Corrections
AUTHOR NAME	Héctor Geffner	user correction - Legacy Corrections
ABSTRACT	pomdps are general models of sequential decisions in which both actions and observations can be probabilistic. Many problems of interest, including extracting decision trees from data, can be formulated as pomdps yet the use of pomdps has been limited by the lack of effective algorithms. Recently this has started to change and a number of problems such as robot navigation and planning are beginning to be formulated and solved as pomdps. The advantage of the pomdp approach is its clean semantics and its ability to produce principled solutions that integrate physical and information gathering actions. In this paper we pursue this approach in the context of two learning tasks: learning to sort a vector of numbers and learning decision trees from data. Both problems are formulated as pomdps and solved by a general pomdp algorithm. The main lessons and results are the following: 1. the use of suitable heuristics and representations allows us to solve sorting and classification pomdps of n...	user correction - Legacy Corrections
YEAR	1998	INFERENCE
VENUE	In Proceedings of the Fifteenth International Conference on Machine Learning	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	73--81	INFERENCE
CITATIONS	21 found	ParsCit 1.0