Learning on opponent's preferences to make effective multi-issue negotiation trade-offs
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Modeling opponent decision in repeated one-shot negotiations
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Predicting partner's behaviour in agent negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Predicting opponent's moves in electronic negotiations using neural networks
Expert Systems with Applications: An International Journal
Empirical game-theoretic analysis of the TAC Supply Chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Opponent modelling in automated multi-issue negotiation using Bayesian learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Genius: negotiation environment for heterogeneous agents
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Negotiation among autonomous computational agents: principles, analysis and challenges
Artificial Intelligence Review
Using Gaussian processes to optimise concession in complex negotiations against unknown opponents
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
ABiNeS: An Adaptive Bilateral Negotiating Strategy over Multiple Items
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
An efficient automated negotiation strategy for complex environments
Engineering Applications of Artificial Intelligence
Conditional restricted Boltzmann machines for negotiations in highly competitive and complex domains
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Complex negotiations among rational autonomous agents is gaining a mass of attention due to the diversity of its possible applications. This paper deals with a prominent type of complex negotiations, namely, multi-issue negotiation that runs under real-time constraints and in which the negotiating agents have no prior knowledge about their opponents' preferences and strategies. We propose a novel negotiation strategy called Dragon which employs sparse pseudo-input Gaussian processes (SPGPs) to model efficiently the behavior of the negotiating opponents. Specifically, with SPGPs Dragon is capable of: (1) efficiently modeling unknown opponents by means of a non-parametric functional prior; (2) significantly reducing the computational complexity of this functional prior; and (3) effectively and adaptively making decisions during negotiation. The experimental results provided in this paper show, both from the standard mean-score perspective and the perspective of empirical game theory, that Dragon outperforms the state-of-the-art negotiation agents from the 2012 and 2011 Automated Negotiating Agents Competition (ANAC) in a variety of negotiation domains.