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» How to Dynamically Merge Markov Decision Processes
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ICML
2004
IEEE
15 years 10 months ago
Apprenticeship learning via inverse reinforcement learning
We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we wa...
Pieter Abbeel, Andrew Y. Ng
ICML
2002
IEEE
15 years 10 months ago
Pruning Improves Heuristic Search for Cost-Sensitive Learning
This paper addresses cost-sensitive classification in the setting where there are costs for measuring each attribute as well as costs for misclassification errors. We show how to ...
Valentina Bayer Zubek, Thomas G. Dietterich
QEST
2006
IEEE
15 years 3 months ago
Compositional Performability Evaluation for STATEMATE
Abstract— This paper reports on our efforts to link an industrial state-of-the-art modelling tool to academic state-of-the-art analysis algorithms. In a nutshell, we enable timed...
Eckard Böde, Marc Herbstritt, Holger Hermanns...
CSL
2010
Springer
14 years 9 months ago
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
This paper explains how Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modelling the inherent uncertainty in spoken di...
Steve Young, Milica Gasic, Simon Keizer, Fran&cced...
CN
2002
127views more  CN 2002»
14 years 9 months ago
Optimal policy for label switched path setup in MPLS networks
An important aspect in designing a multiprotocol label switching (MPLS) network is to determine an initial topology and to adapt it to the traffic load. A topology change in an MP...
Tricha Anjali, Caterina M. Scoglio, Jaudelice Cava...