A Contextual Combinatorial Bandit Approach to Negotiation
Authors: Yexin Li, Zhancun Mu, Siyuan Qi
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on three negotiation tasks demonstrate the superiority of our approach. |
| Researcher Affiliation | Collaboration | 1State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China 2Peking University. Correspondence to: Siyuan Qi <syqi@bigai.ai>. |
| Pseudocode | Yes | Algorithm 1 Neg UCB Algorithm |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code available or links to a code repository. |
| Open Datasets | Yes | ANAC (Automated Negotiating Agents Competition) is an international tournament that has been held since 2010, providing 50 negotiation domains. |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits with percentages, sample counts, or specific split methodologies. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages with library versions or solver versions). |
| Experiment Setup | Yes | We conduct a search and summarize the six representative exploration rates corresponding to Figure 4(a) in Table 2, where the rates in bold are the optimal ones of each algorithm. ... we systematically explore SE kernels with diverse hyper-parameters σ, specifically σ = 0.5, 1, 2, and 5, to fine-tune the most suitable kernel function for the technology trading task in Civ Realm. |