A Contextual Combinatorial Bandit Approach to Negotiation
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913411482255360 |
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| author | Li, Yexin Mu, Zhancun Qi, Siyuan |
| author_facet | Li, Yexin Mu, Zhancun Qi, Siyuan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_00567 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Contextual Combinatorial Bandit Approach to Negotiation Li, Yexin Mu, Zhancun Qi, Siyuan Artificial Intelligence Machine Learning 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. |
| title | A Contextual Combinatorial Bandit Approach to Negotiation |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.00567 |