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Partially observable Markov decision processes for spoken dialog systems Jason D. Williams, Steve Young (AT&T Labs) 2007, Computer Speech and Language, 21(2):
Outline ,[object Object]
Partially observable Markov decision processes
Spoken Dialog System
SDS-POMDP
Comparing
Empirical support
POMDP (1) ,[object Object]
POMDP = {S, A, T, R, O, Z,  λ, b 0 } ,[object Object]
A – set of actions, that agent may take
T – transition probability – P(s'|s, a)
R – reward – r(s, a)
O – set of observation about the world
Z – observation probability – P(o'|s', a)
POMDP (2) ,[object Object]
b 0  – initial belief state   b 0 (s)
POMDP (3) ,[object Object]
□  - decision node
◊  - utility node
Shaded – unobserved
| - causal effect
  - distribution is used
RL – reinforced learning
POMDP (Example) ,[object Object]
Spoken Dialog System ,[object Object]
S d  – dialog state (user view)
A u  – user action (intention)
Spoken Dialog System ,[object Object]
A u  – action recognized by machine
C – confidence score
S m  – dialog state (machine view) ~
Spoken Dialog System ,[object Object]
Y m  – machine audio signal
A m  – action recognized by user ~
Mapping SDS to POMDP ,[object Object]
SDS = {S u , S d , S m , C, A u , A u , A m } ~
SDS-POMDP ,[object Object]

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Partially observable Markov decision processes for spoken dialog systems