Models of human behaviours used in multi-agent simulations are limited
by the ability of introspection of the social actors: some of their
knowledge (reflexes, habits, non-formalized expertise) cannot be
extracted through interviews. In this paper, we propose an artificial
maieutic approach to extract such pieces of knowledge, by helping
the actors to better understand, and sometimes formulate, their own
behaviours. We present here the first results using two complementary
works in social simulations. one in the domain of air traffic control
and one in the domain of common-pool resources sharing.
%0 Conference Paper
%1 Sempe:2005:aamas
%A Sempé, Fran\,cois
%A Nguyen-Duc, Minh
%A Boucher, Alain
%A Drogoul, Alexis
%B AAMAS'05: Proc. 4th Int'l Jt. Conf. on
Autonomous Agents and Multiagent Systems
%C Utrecht, Netherlands
%D 2005
%E Dignum, F.
%E Dignum, V.
%E Koenig, S.
%E Kraus, S.
%E Singh, M. P.
%E Wooldridge, M.
%I ACM Press
%K imported thesis
%P 1361--1362
%R 10.1145/1082473.1082773
%T An artificial maieutic approach for eliciting experts' knowledge
in multi-agent simulations
%X Models of human behaviours used in multi-agent simulations are limited
by the ability of introspection of the social actors: some of their
knowledge (reflexes, habits, non-formalized expertise) cannot be
extracted through interviews. In this paper, we propose an artificial
maieutic approach to extract such pieces of knowledge, by helping
the actors to better understand, and sometimes formulate, their own
behaviours. We present here the first results using two complementary
works in social simulations. one in the domain of air traffic control
and one in the domain of common-pool resources sharing.
%@ 1-59593-093-0
@inproceedings{Sempe:2005:aamas,
abstract = {Models of human behaviours used in multi-agent simulations are limited
by the ability of introspection of the social actors: some of their
knowledge (reflexes, habits, non-formalized expertise) cannot be
extracted through interviews. In this paper, we propose an artificial
maieutic approach to extract such pieces of knowledge, by helping
the actors to better understand, and sometimes formulate, their own
behaviours. We present here the first results using two complementary
works in social simulations. one in the domain of air traffic control
and one in the domain of common-pool resources sharing.},
added-at = {2017-03-16T11:50:55.000+0100},
address = {Utrecht, Netherlands},
author = {Semp\'e, Fran\{,}cois and Nguyen-Duc, Minh and Boucher, Alain and Drogoul, Alexis},
biburl = {https://www.bibsonomy.org/bibtex/2e6c5768036ea2763ddefa8534dc91a11/krevelen},
booktitle = {AAMAS'05: Proc. 4th Int'l Jt. Conf. on
Autonomous Agents and Multiagent Systems},
crossref = {aamas:2005},
doi = {10.1145/1082473.1082773},
editor = {Dignum, F. and Dignum, V. and Koenig, S. and Kraus, S. and Singh, M. P. and Wooldridge, M.},
interhash = {8c922ab5055cf13226d4d96023c48c96},
intrahash = {e6c5768036ea2763ddefa8534dc91a11},
isbn = {1-59593-093-0},
keywords = {imported thesis},
owner = {Rick},
pages = {1361--1362},
publisher = {ACM Press},
publisher_address = {New York},
timestamp = {2017-03-16T11:54:14.000+0100},
title = {An artificial maieutic approach for eliciting experts' knowledge
in multi-agent simulations},
year = 2005
}