Efficient model learning for dialog management

Intelligent planning algorithms such as the Partially Observable Markov Decision Process (POMDP) have succeeded in dialog management applications [10, 11, 12] because they are robust to the inherent uncertainty of human interaction. Like all dialog planning systems, however, POMDPs require an accura...

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Vydané v:	2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI) s. 65 - 72
Hlavní autori:	Doshi, Finale, Roy, Nicholas
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	New York, NY, USA ACM 10.03.2007 IEEE
Edícia:	ACM Conferences
Predmet:	Abstracts Computing methodologies > Machine learning > Learning paradigms Computing methodologies > Machine learning > Machine learning approaches > Markov decision processes Convergence decision-making under uncertainty Face History Human-robot interaction model learning Planning Pragmatics Robots Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes human-robot interaction decision-making under uncertainty model learning
ISBN:	1595936173, 9781595936172
ISSN:	2167-2121
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