A Machine Learning Based Evaluation of a Negotiation between Agents Involving Fuzzy Counter-Offers. Rubiera, J. C. & Ledezma, A. In Ruiz, E. M., Segovia, J., & Szczepaniak, P. S., editors, First International Atlantic Web Intelligence Conference, AWIC 2003, volume 2663, of Lecture Notes in Computer Science, pages 268–277, Madrid, España, May, 2003. Springer Berlin / Heidelberg. abstract bibtex Negotiation plays a fundamental role in systems composed of multiple autonomous agents. Some negotiations may require a more elaborated dialogue where agents would explain offer rejections in a general and vague way. We propose that agents would represent their disappointment about an offer through a fuzzy set applied to each attribute of the offer. Fuzziness can also be very useful in order to make user profiles more difficult to acquire. The satisfaction of this intention is evaluated using classification techniques to compare the accuracy of the models that were obtained from the observation of the behaviour of the agents. In order to test how much information may be extracted about the internal preferences of agents, the task of modeling is translated into a classification task solved by a technique that would generate symbolic representations, such as m5.
@INPROCEEDINGS{fuzzy-awic03,
author = {Javier Carbo Rubiera and Agapito Ledezma},
title = {A Machine Learning Based Evaluation of a Negotiation between Agents
Involving Fuzzy Counter-Offers},
booktitle = {First International Atlantic Web Intelligence Conference, AWIC 2003},
year = {2003},
editor = {Ernestina Menasalvas Ruiz and Javier Segovia and Piotr S. Szczepaniak},
volume = {2663},
series = {Lecture Notes in Computer Science},
pages = {268--277},
address = {Madrid, España},
month = {May},
publisher = {Springer Berlin / Heidelberg},
abstract = {Negotiation plays a fundamental role in systems composed of multiple
autonomous agents. Some negotiations may require a more elaborated
dialogue where agents would explain offer rejections in a general
and vague way. We propose that agents would represent their disappointment
about an offer through a fuzzy set applied to each attribute of the
offer. Fuzziness can also be very useful in order to make user profiles
more difficult to acquire. The satisfaction of this intention is
evaluated using classification techniques to compare the accuracy
of the models that were obtained from the observation of the behaviour
of the agents. In order to test how much information may be extracted
about the internal preferences of agents, the task of modeling is
translated into a classification task solved by a technique that
would generate symbolic representations, such as m5.},
bib2html_pubtype = {Refereed Conference},
bib2html_rescat = {Agent Modeling},
days = {5--6},
isbn = {3-540-40124-5},
key = {modelado},
owner = {ledezma},
timestamp = {2006.07.05}
}
Downloads: 0
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