Dataset of User Study "Artificial Trust Communication in a 2D grid-world Collaborative Search and Rescue Scenario"

Context: Communication and mutual trust are keys driver for effective teamwork in human teams. In human-AI teams, teams composed of both humans and artificial agents, communication and trust are also important. In this research project, we investigated how different artificial agent’s communication affect human’s trust and satisfaction, in such teams. Participants teamed up with artificial agents in an online setting (using 2D grid world) and their decisions were be logged. This dataset includes different metrics calculated based on the logs, self-reported questionnaire answers on trust and satisfaction, and free answers to open questions.<br>This dataset was created during the Research Project course of the Computer Science Bachelor's in Delft University of Technology supervised by Carolina Jorge and Dr. Myrthe Tielman. Five students ran a user study with six different conditions (the baseline, and five new developed by each of them). The full description of the user study and their individual results (i.e., pairwise comparison between their own condition and baseline) can be found in each of their thesis, linked in this page below. <br>Then, a full joint dataset was created and it can be found in "Full dataset.csv" (total 140 rows). To balance the number of participants per condition, we generated a "capped_dataset.csv" with 20 rows per condition (total N=120). We analysed differences among conditions, and rerun the pairwise comparisons, of "capped_dataset.csv". The code can be found in "Quantitative Analysis.ipynb". These results are to be published in a paper - the author contributions can be found in "author_contribution.txt".<br>The full code used for the generation of this dataset can be found in this Github repository: https://github.com/centeio/AT-Communication <br>

研究背景：沟通与互信是人类团队实现高效协作的核心驱动力。在人机混合团队——即由人类与人工代理（artificial agents）共同组成的团队——中，沟通与信任同样至关重要。本研究项目针对此类团队，探究了不同人工代理的沟通模式对人类成员的信任度与满意度的影响。研究参与者在在线环境下（基于二维网格世界（2D grid world））与人工代理组队完成任务，其决策过程均被记录存档。本数据集包含基于日志计算得到的各类指标、关于信任度与满意度的自评问卷结果，以及开放式问题的自由作答内容。
本数据集由代尔夫特理工大学（Delft University of Technology）计算机科学本科研究项目课程生成，由Carolina Jorge与Myrthe Tielman博士指导。五名学生开展了一项用户研究，共设置6种实验条件（1种基线条件与5种由每名学生分别开发的新条件）。该用户研究的完整说明以及各学生的单独研究结果（即其对应实验条件与基线条件的配对比较结果），可在下方链接指向的各学生学位论文中查阅。
随后，我们整合生成了完整联合数据集，存储于"Full dataset.csv"中（共140条记录）。为平衡各实验条件下的参与者数量，我们生成了"capped_dataset.csv"，每个条件包含20条记录（总样本量N=120）。我们基于"capped_dataset.csv"分析了各实验条件间的差异，并重新开展了配对比较分析，相关代码存储于"Quantitative Analysis.ipynb"中。上述研究结果将发表于一篇学术论文，作者贡献说明可参阅"author_contribution.txt"。
本数据集生成所用的完整代码可在以下GitHub仓库获取：https://github.com/centeio/AT-Communication