VeNAS: Versatile Negotiating Agent Strategy via Deep Reinforcement Learning (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v36i11.21669Keywords:
Automated Negotiation, Reinforcement Learning, Negotiation StrategyAbstract
Existing research in the field of automated negotiation considers a negotiation architecture in which some of the negotiation components are designed separately by reinforcement learning (RL), but comprehensive negotiation strategy design has not been achieved. In this study, we formulated an RL model based on a Markov decision process (MDP) for bilateral multi-issue negotiations. We propose a versatile negotiating agent that can effectively learn various negotiation strategies and domains through comprehensive strategies using deep RL. We show that the proposed method can achieve the same or better utility than existing negotiation agents.Downloads
Published
2022-06-28
How to Cite
Takahashi, T., Higa, R., Fujita, K., & Nakadai, S. (2022). VeNAS: Versatile Negotiating Agent Strategy via Deep Reinforcement Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13065-13066. https://doi.org/10.1609/aaai.v36i11.21669
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Section
AAAI Student Abstract and Poster Program