Human-Robot Trust and Cooperation Through a Game Theoretic Framework
Authors: Erin Paeng, Jane Wu, James Boerkoel
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical analysis shows that humans tend to trust robots to a greater degree than other humans, while cooperating equally well in both. |
| Researcher Affiliation | Academia | Harvey Mudd College, Claremont, CA {epaeng, jhwu, boerkoel}@g.hmc.edu |
| Pseudocode | Yes | Algorithm 1: Coin Entrustment |
| Open Source Code | No | No explicit statement or link regarding the availability of open-source code for the described methodology was found. |
| Open Datasets | No | The data was collected from Amazon's Mechanical Turk, but no specific access information (link, DOI, repository, or formal citation to a public dataset) for the collected dataset is provided. |
| Dataset Splits | No | The paper describes a human-robot interaction experiment involving game rounds, not a machine learning setup with explicit training, validation, or test dataset splits. |
| Hardware Specification | No | No specific hardware details (GPU/CPU models, memory, or cloud instance specifications) used for running experiments or analysis were mentioned. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | Our algorithm cooperates on the first round, and defects only if the opponent has defected twice in a row. To explore both the initial emergence of trust and cooperation and its reemergence after a betrayal of trust, our strategy also defects on round 8 if it has not already defected in the previous rounds. |