Summary of A Contextual Combinatorial Bandit Approach to Negotiation, by Yexin Li and Zhancun Mu and Siyuan Qi
A Contextual Combinatorial Bandit Approach to Negotiation
by Yexin Li, Zhancun Mu, Siyuan Qi
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach to tackle various negotiation problems is introduced in this paper. The proposed method leverages contextual combinatorial multi-armed bandits to resolve the exploration-exploitation dilemma and handle large action spaces, effectively addressing two key challenges in negotiation. The novel NegUCB method handles common issues such as partial observations and complex reward functions in negotiation, ensuring a sub-linear regret upper bound under mild assumptions. Experimental results on three negotiation tasks demonstrate the superiority of our approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Negotiation strategies are important to learn, but there’s a challenge: exploring new options versus sticking with what you know. This paper introduces a way to tackle this problem and many others in negotiation. It uses a special type of math called multi-armed bandits to help decide which actions to take. The approach is tested on three different negotiation scenarios and shows that it works better than other methods. |
Keywords
» Artificial intelligence » Machine learning
