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Using neural networks to minimize the duration of automated negotiation threads for hybrid opponents

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Using neural networks to minimize the duration of automated negotiation threads for hybrid opponents (EN)
Papaioannou, I (EN)
Roussaki, I (EN)
Anagnostou, M (EN)
journalArticle (EN)
2014-03-01T01:34:49Z
2010 (EN)
Neural networks (NNs) provide an efficient tool that can be trained to estimate the value of output parameters given certain metrics. In this paper, NNs are used to enhance intelligent agents that negotiate on behalf of their owners aiming to maximize their utility. More specifically, NNs are exploited in order to predict the hybrid negotiation behavior of the agents' opponents, thus achieving more profitable results for the parties these agents represent. The NNs provide the means so that the agents can early detect the cases where agreements are not achievable, thus supporting their decision to withdraw or not from the negotiation threads. The designed NN-assisted negotiation strategies have been evaluated via extensive experiments and are proven to be very useful, as they manage to significantly reduce the average duration of the negotiation threads. © 2010 World Scientific Publishing Company. (EN)
Engineering, Electrical & Electronic (EN)
Computer Science, Hardware & Architecture (EN)
MLP and GR neural networks (EN)
Negotiation strategy (EN)
Automation (EN)
Profitability (EN)
Opponent behavior prediction (EN)
Hybrid negotiation strategies (EN)
Behavior prediction (EN)
Forecasting (EN)
Automated negotiations (EN)
Output parameters (EN)
Neural networks (EN)
Intelligent agents (EN)
Elimination of unsuccessful negotiations (EN)
Journal of Circuits, Systems and Computers (EN)
English
WORLD SCIENTIFIC PUBL CO PTE LTD (EN)
National Technical University of Athens
Digital Library of National Technical University of Athens | Dspace@NTUA
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