Text

Building Dialogue POMDPs from Expert Dialogues

---

This book discusses the Partially Observable Markov Decision Process (POMDP) framework applied in dialogue systems. It presents POMDP as a formal framework to represent uncertainty explicitly while supporting automated policy solving. The authors propose and implement an end-to-end learning approach for dialogue POMDP model components. Starting from scratch, they present the state, the transition model, the observation model and then finally the reward model from unannotated and noisy dialogues. These altogether form a significant set of contributions that can potentially inspire substantial further work. This concise manuscript is written in a simple language, full of illustrative examples, figures, and tables.

---

Availability

No copy data

Detail Information

Series Title

-

Call Number

620

Publisher

Cham : Springer Cham., 2016

Collation

-

Language

English

ISBN/ISSN

-

Classification

620

Content Type

-

Media Type

computer

Carrier Type

-

Edition

-

Subject(s)

Engineering

Specific Detail Info

-

Statement of Responsibility

Hamidreza Chinaei, Brahim Chaib-draa

Other Information

Cataloger

ratna

Source

https://link.springer.com/book/10.1007/978-3-319-26200-0

Validator

-

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment