This study tested the capability of artificial social intelligence to make inferences and predictions concerning the state and behaviors of 3-person teams executing an urban search and rescue task in Minecraft.

The ASIST Study-2 dataset was developed in a human subjects research study designed to assess the capability of artificial intelligence to infer the state and predict the actions of members of a three-person team executing an urban search and rescue task in Minecraft. The data were developed under Contract No. HR001119C0130 to the Defense Advanced Research Projects Agency (DARPA). The dataset comprises approximately 2,100 files and 300GB of data. We have partitioned the full dataset into folders that support research in specific areas. Thus, researchers can more easily download only the files of value to them.
Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

1 to 3 of 3 Results
Artificial Social Intelligence for Successful Teams (ASIST) Study 4 Dragon Testbed Dataset
Feb 7, 2024
Lixiao Huang; Adam Fouse; Nancy Cooke; Edward Weiss, 2024, "Artificial Social Intelligence for Successful Teams (ASIST) Study 4 Dragon Testbed Dataset", https://doi.org/10.48349/ASU/ZO6XVR, ASU Library Research Data Repository, V2, UNF:6:jkhVIRagnIe25M/7ClVYUg== [fileUNF]
Artificial Social Intelligence for Successful Teams (ASIST) Study 4 Dragon Testbed Dataset was developed in a human subjects research study designed to assess the capability of artificial intelligence to instantiate a Machine Theory of Teams and apply it to generate and issue (or...
Artificial Social Intelligence for Successful Teams (ASIST) Study 3
Artificial Social Intelligence for Successful Teams (ASIST) Study 3
Jan 3, 2023
Lixiao Huang; Jared Freeman; Nancy Cooke; John “JCR” Colonna-Romano; Matt Wood; Verica Buchanan; Stephen Caufman, 2022, "Artificial Social Intelligence for Successful Teams (ASIST) Study 3", https://doi.org/10.48349/ASU/QDQ4MH, ASU Library Research Data Repository, V3, UNF:6:M0oVzTeD5aBRBm/AbSdJ/g== [fileUNF]
The ASIST Study-3 dataset was developed in a human subjects research study designed to assess the capability of artificial intelligence to instantiate a Machine Theory of Teams, and apply it to generate and issue (or withold) advice to team members that improve team state (e.g.,...
Artificial Social Intelligence for Successful Teams (ASIST) Study 2
May 26, 2022
Lixiao Huang; Jared Freeman; Nancy Cooke; Samantha Dubrow; John “JCR” Colonna-Romano; Matt Wood; Verica Buchanan; Stephen Caufman; Xiaoyun Yin, 2022, "Artificial Social Intelligence for Successful Teams (ASIST) Study 2", https://doi.org/10.48349/ASU/BZUZDE, ASU Library Research Data Repository, V4, UNF:6:OJ3XctVE31iBZs09zhPpFQ== [fileUNF]
The ASIST Study-2 dataset was developed in a human subjects research study designed to assess the capability of artificial intelligence to infer the state and predict the actions of members of a three-person team executing an urban search and rescue task in Minecraft. The data we...
Add Data

Log in to create a dataverse or add a dataset.

Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.