Predicting partner's behaviour in agent negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Predicting opponent's moves in electronic negotiations using neural networks
Expert Systems with Applications: An International Journal
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Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Factored conditional restricted Boltzmann Machines for modeling motion style
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Genius: negotiation environment for heterogeneous agents
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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
Optimizing complex automated negotiation using sparse pseudo-input gaussian processes
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Learning in automated negotiations, while useful, is hard because of the indirect way the target function can be observed and the limited amount of experience available to learn from. This paper proposes two novel opponent modeling techniques based on deep learning methods. Moreover, to improve the learning efficacy of negotiating agents, the second approach is also capable of transferring knowledge efficiently between negotiation tasks. Transfer is conducted by automatically mapping the source knowledge to the target in a rich feature space. Experiments show that using these techniques the proposed strategies outperform existing state-of-the-art agents in highly competitive and complex negotiation domains. Furthermore, the empirical game theoretic analysis reveals the robustness of the proposed strategies.