Control, Capabilities and Communication: Three Key Issues for Machine-Expert Collaborative Knowledge Acquisition
G. Webb. Proceedings (Complement) of the Seventh European Workshop on Knowledge Acquisition for Knowledge-based Systems (EWKA'93), page 263-275. Springer-Verlag, (1993)
Abstract
Machine learning and knowledge elicitation are different but complementary approaches to knowledge acquisition. On the face of it there are large potential gains to be reaped from the integration of these two knowledge acquisition techniques. Machine-expert collaborative knowledge acquisition combines these approaches by placing the machine learning system and the human expert as partners in the knowledge-acquisition task. This paper examines three key issues facing machine-expert collaborative knowledge-acquisition where should control reside, what capabilities should each partner bring to the task and how should the partners communicate?
%0 Conference Paper
%1 Webb93e
%A Webb, G. I.
%B Proceedings (Complement) of the Seventh European Workshop on Knowledge Acquisition for Knowledge-based Systems (EWKA'93)
%D 1993
%E Aussenac, N.
%E Boy, G.
%E Gaines, B.
%E Linster, M.
%E Ganascia, J.G.
%E Kodratoff, Y.
%I Springer-Verlag
%K Acquisition Experts Knowledge Learning Machine from with
%P 263-275
%T Control, Capabilities and Communication: Three Key Issues for Machine-Expert Collaborative Knowledge Acquisition
%X Machine learning and knowledge elicitation are different but complementary approaches to knowledge acquisition. On the face of it there are large potential gains to be reaped from the integration of these two knowledge acquisition techniques. Machine-expert collaborative knowledge acquisition combines these approaches by placing the machine learning system and the human expert as partners in the knowledge-acquisition task. This paper examines three key issues facing machine-expert collaborative knowledge-acquisition where should control reside, what capabilities should each partner bring to the task and how should the partners communicate?
@inproceedings{Webb93e,
abstract = {Machine learning and knowledge elicitation are different but complementary approaches to knowledge acquisition. On the face of it there are large potential gains to be reaped from the integration of these two knowledge acquisition techniques. Machine-expert collaborative knowledge acquisition combines these approaches by placing the machine learning system and the human expert as partners in the knowledge-acquisition task. This paper examines three key issues facing machine-expert collaborative knowledge-acquisition where should control reside, what capabilities should each partner bring to the task and how should the partners communicate? },
added-at = {2016-03-20T05:42:04.000+0100},
audit-trail = {*},
author = {Webb, G. I.},
biburl = {https://www.bibsonomy.org/bibtex/2d679803babb6cb98d93faa493d03e54e/giwebb},
booktitle = {Proceedings (Complement) of the Seventh European Workshop on Knowledge Acquisition for Knowledge-based Systems (EWKA'93)},
editor = {Aussenac, N. and Boy, G. and Gaines, B. and Linster, M. and Ganascia, J.G. and Kodratoff, Y.},
interhash = {9b7317e34b818716c09e382baaa5c65f},
intrahash = {d679803babb6cb98d93faa493d03e54e},
keywords = {Acquisition Experts Knowledge Learning Machine from with},
location = {Toulouse, France},
pages = {263-275},
publisher = {Springer-Verlag},
timestamp = {2016-03-20T05:42:04.000+0100},
title = {Control, Capabilities and Communication: Three Key Issues for Machine-Expert Collaborative Knowledge Acquisition},
year = 1993
}