Proceedings of the 3rd International Conference on Computer Science and Service System

Gaussian process regression prediction-based dynamic risk negotiation strategy

Authors
Hu Jun, Zou Li
Corresponding Author
Hu Jun
Available Online June 2014.
DOI
10.2991/csss-14.2014.18How to use a DOI?
Keywords
multi-issue negotiation; Gaussian process regression; dynamic risk strategy; concession strategy
Abstract

In this paper, we use Gaussian process regression to predict the opponent concessions, and introduce the dynamic risk mechanism in Agent negotiation. We can change the risk factor by the utility of opponents and set a threshold in this dynamic risk mechanism. Dynamic risk strategies associated with opponent concessions prediction can not only dynamically change the risk attitude according to the utility of opponents, but also obtain a higher utility value in the negotiation. We establish this negotiation model and run it in the Generic Environment for Negotiation with Intelligent multi-purpose Usage Simulation (GENIUS), and the results prove the performance of our Agent is superior to other Agents.

Copyright
© 2014, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 3rd International Conference on Computer Science and Service System
Series
Advances in Intelligent Systems Research
Publication Date
June 2014
ISBN
10.2991/csss-14.2014.18
ISSN
1951-6851
DOI
10.2991/csss-14.2014.18How to use a DOI?
Copyright
© 2014, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Hu Jun
AU  - Zou Li
PY  - 2014/06
DA  - 2014/06
TI  - Gaussian process regression prediction-based dynamic risk negotiation strategy
BT  - Proceedings of the 3rd International Conference on Computer Science and Service System
PB  - Atlantis Press
SP  - 80
EP  - 83
SN  - 1951-6851
UR  - https://doi.org/10.2991/csss-14.2014.18
DO  - 10.2991/csss-14.2014.18
ID  - Jun2014/06
ER  -