Opponent Modelling in Persuasion Dialogues
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 164-169.
Abstract
A strategy is used by a participant in a persuasion dialogue to select locutions most likely to achieve its objective of persuading its opponent. Such strategies often assume that the participant has a model of its opponents, which may be constructed on the basis of a participant's accumulated dialogue experience. However in most cases the fact that an agent's experience may encode additional information which if appropriately used could increase a strategy's efficiency, is neglected. In this work, we rely on an agent's experience to define a mechanism for augmenting an opponent model with information likely to be dialectally related to information already contained in it. Precise computation of this likelihood is exponential in the volume of related information. We thus describe and evaluate an approximate approach for computing these likelihoods based on Monte-Carlo simulation.
@inproceedings{ijcai2013p34,
title = {Opponent Modelling in Persuasion Dialogues},
author = {Hadjinikolis, Christos and Siantos, Yiannis and Modgil, Sanjay and Black, Elizabeth and McBurney, Peter},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {164--169},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/34},
}
TY - CPAPER TI - Opponent Modelling in Persuasion Dialogues AU - Hadjinikolis, Christos AU - Siantos, Yiannis AU - Modgil, Sanjay AU - Black, Elizabeth AU - McBurney, Peter T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 164 EP - 169 UR - https://ijcai.org/proceedings/2013/34 ER -Copied
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