Targeted Data Acquisition for Evolving Negotiation Agents
Authors: Minae Kwon, Siddharth Karamcheti, Mariano-Florentino Cuellar, Dorsa Sadigh
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate Alice subject to these desiderata through experiments against simulated and real-human partners. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Stanford University, Stanford, CA 2School of Law, Stanford University, Stanford, CA. |
| Pseudocode | No | The paper describes methods in text but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement about releasing open-source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | We evaluate our framework on the DEALORNODEAL negotiation task (Lewis et al., 2017) |
| Dataset Splits | No | The paper mentions using specific datasets (DL, DH) for training and evaluation against an expert, and reports results over random seeds, but does not provide explicit numerical train/validation/test splits (e.g., percentages or exact counts) to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not specify the exact GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software components or libraries used in the experiments. |
| Experiment Setup | No | The paper states, "Implementation details can be found in the supplementary." (Section 4) but does not provide specific hyperparameters or system-level training settings in the main text. |