A Contextual Combinatorial Bandit Approach to Negotiation
<|reference_start|>A Contextual Combinatorial Bandit Approach to Negotiation: Learning effective negotiation strategies poses two key challenges: the exploration-exploitation dilemma and dealing with large action spaces. However, there is an absence of learning-based approaches that effectively address these challenges in negotiation. This paper introduces a comprehensive formulation to tackle various negotiation problems. Our approach leverages contextual combinatorial multi-armed bandits, with the bandits resolving the exploration-exploitation dilemma, and the combinatorial nature handles large action spaces. Building upon this formulation, we introduce NegUCB, a novel method that also handles common issues such as partial observations and complex reward functions in negotiation. NegUCB is contextual and tailored for full-bandit feedback without constraints on the reward functions. Under mild assumptions, it ensures a sub-linear regret upper bound. Experiments conducted on three negotiation tasks demonstrate the superiority of our approach.<|reference_end|>
@article{li2024a,
    title={A Contextual Combinatorial Bandit Approach to Negotiation},
    author={Yexin Li and Zhancun Mu and Siyuan Qi},
    journal={arXiv preprint arXiv:2407.00567},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00567},
    primaryClass={cs.AI cs.LG}
}