N. Blaylock, и J. Allen. IJCAI'03: Proceedings of the 18th international joint conference on Artificial intelligence, стр. 1303--1308. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (2003)
Аннотация
Goal recognition for dialogue systems needs to be fast, make early predictions, and be portable. We present initial work which shows that using statistical, corpus-based methods to build goal recognizers may be a viable way to meet those needs. Our goal recognizer is trained on data from apian corpus and then used to determine the agent's most likely goal based on that data. The algorithm is linear in the number of goals, and performs very well in terms of accuracy and early prediction. In addition, it is more easily portable to new domains as does not require a hand-crafted plan library.
%0 Conference Paper
%1 Blaylock03
%A Blaylock, Nate
%A Allen, James
%B IJCAI'03: Proceedings of the 18th international joint conference on Artificial intelligence
%C San Francisco, CA, USA
%D 2003
%I Morgan Kaufmann Publishers Inc.
%K Intent
%P 1303--1308
%T Corpus-based, statistical goal recognition
%U http://portal.acm.org/citation.cfm?id=1630845
%X Goal recognition for dialogue systems needs to be fast, make early predictions, and be portable. We present initial work which shows that using statistical, corpus-based methods to build goal recognizers may be a viable way to meet those needs. Our goal recognizer is trained on data from apian corpus and then used to determine the agent's most likely goal based on that data. The algorithm is linear in the number of goals, and performs very well in terms of accuracy and early prediction. In addition, it is more easily portable to new domains as does not require a hand-crafted plan library.
@inproceedings{Blaylock03,
abstract = {Goal recognition for dialogue systems needs to be fast, make early predictions, and be portable. We present initial work which shows that using statistical, corpus-based methods to build goal recognizers may be a viable way to meet those needs. Our goal recognizer is trained on data from apian corpus and then used to determine the agent's most likely goal based on that data. The algorithm is linear in the number of goals, and performs very well in terms of accuracy and early prediction. In addition, it is more easily portable to new domains as does not require a hand-crafted plan library.},
added-at = {2010-04-30T14:10:51.000+0200},
address = {San Francisco, CA, USA},
author = {Blaylock, Nate and Allen, James},
biburl = {https://www.bibsonomy.org/bibtex/221fc7c70b37980faa4e980753eeeb67a/mkroell},
booktitle = {IJCAI'03: Proceedings of the 18th international joint conference on Artificial intelligence},
description = {Corpus-based, statistical goal recognition},
interhash = {5e11ece70ad5a5e43eb7cb91a242073f},
intrahash = {21fc7c70b37980faa4e980753eeeb67a},
keywords = {Intent},
location = {Acapulco, Mexico},
pages = {1303--1308},
publisher = {Morgan Kaufmann Publishers Inc.},
timestamp = {2010-04-30T14:10:51.000+0200},
title = {Corpus-based, statistical goal recognition},
url = {http://portal.acm.org/citation.cfm?id=1630845},
year = 2003
}