Building a Synthetic Task Environment to Support Artificial Social Intelligence Research

2021 ◽  
Vol 65 (1) ◽  
pp. 660-664
Author(s):  
Christopher C. Corral ◽  
Keerthi Shrikar Tatapudi ◽  
Verica Buchanan ◽  
Lixiao Huang ◽  
Nancy J. Cooke
Keyword(s):  
Theory Of Mind ◽  
Data Collection ◽  
Real Time ◽  
Social Intelligence ◽  
Search And Rescue ◽  
Task Environment ◽  
Urban Search And Rescue ◽  
Rich Data ◽  
Synthetic Task Environment ◽  
Support Research

To support research on artificial social intelligence for successful teams (ASIST), an urban search and rescue task (USAR) was simulated within Minecraft to serve as a Synthetic Task Environment (STE). The goal for the development of the present STE was to create an environment that provides ample opportunities to allow ASI agents to demonstrate the theory of mind by making inferences and predictions of humans’ states and actions in the USAR task environment, and in the future to intervene to improve teamwork in real-time. This paper describes the STE design background, design potentials and considerations, rich data collection opportunities, and potential usage for more broad research.

scholarly journals Developing Human-Robot Team Interdependence in a Synthetic Task Environment

2019 ◽  
Vol 63 (1) ◽  
pp. 1503-1507 ◽  
Author(s):  
Glenn J. Lematta ◽  
Pamela B. Coleman ◽  
Shawaiz A. Bhatti ◽  
Erin K. Chiou ◽  
Nathan J. McNeese ◽  
...  
Keyword(s):  
Human Factors ◽  
Search And Rescue ◽  
Cognitive Engineering ◽  
Cognitive Tasks ◽  
Task Environment ◽  
Urban Search And Rescue ◽  
Victim Identification ◽  
Team Task ◽  
Synthetic Task Environment ◽  
Team Interdependence

In future urban search and rescue teams, robots may be expected to conduct cognitive tasks. As the capabilities of robots change, so too will their interdependence with human teammates. Human factors and cognitive engineering are well-positioned to guide the design of autonomy for effective teaming. Previous work in the urban search and rescue synthetic task environment (USAR-STE) used Minecraft, a customizable gaming platform. In this effort, we advanced the USAR-STE by increasing interdependence in dyadic human-robot teaming through the Coactive Design framework. In this framework, we defined required capacities of victim identification in USAR from literature, and used them as inputs for modeling interdependence, and determined recommendations that would enhance interdependence in the task environment. Although Coactive Design is typically used to design interdependence for robots or jobs, we demonstrated how it can also be used to design an experimental team task environment.

scholarly journals Real-time localization and elevation mapping within urban search and rescue scenarios

2007 ◽  
Vol 24 (8-9) ◽  
pp. 723-745 ◽  
Author(s):  
Alexander Kleiner ◽  
Christian Dornhege
Keyword(s):  
Real Time ◽  
Search And Rescue ◽  
Urban Search And Rescue

Enhancing Clinical Supervision Using High-Definition Real-Time Digital Recording, Annotation, and Bluetooth Technology

2019 ◽  
Vol 4 (2) ◽  
pp. 356-362
Author(s):  
Jennifer W. Means ◽  
Casey McCaffrey
Keyword(s):  
Graduate Students ◽  
Data Collection ◽  
Real Time ◽  
Clinical Supervision ◽  
Video Recording ◽  
Advanced Technology ◽  
High Definition ◽  
Recording Technology ◽  
Self Confidence ◽  
Additional Data Collection

Purpose The use of real-time recording technology for clinical instruction allows student clinicians to more easily collect data, self-reflect, and move toward independence as supervisors continue to provide continuation of supportive methods. This article discusses how the use of high-definition real-time recording, Bluetooth technology, and embedded annotation may enhance the supervisory process. It also reports results of graduate students' perception of the benefits and satisfaction with the types of technology used. Method Survey data were collected from graduate students about their use and perceived benefits of advanced technology to support supervision during their 1st clinical experience. Results Survey results indicate that students found the use of their video recordings useful for self-evaluation, data collection, and therapy preparation. The students also perceived an increase in self-confidence through the use of the Bluetooth headsets as their supervisors could provide guidance and encouragement without interrupting the flow of their therapy sessions by entering the room to redirect them. Conclusions The use of video recording technology can provide opportunities for students to review: videos of prospective clients they will be treating, their treatment videos for self-assessment purposes, and for additional data collection. Bluetooth technology provides immediate communication between the clinical educator and the student. Students reported that the result of that communication can improve their self-confidence, perceived performance, and subsequent shift toward independence.

scholarly journals Costing interventions in the field: preliminary cost estimates and lessons learned from an evaluation of community-wide mass drug administration for elimination of soil-transmitted helminths in the DeWorm3 trial

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e049734
Author(s):  
Katya Galactionova ◽  
Maitreyi Sahu ◽  
Samuel Paul Gideon ◽  
Saravanakumar Puthupalayam Kaliappan ◽  
Chloe Morozoff ◽  
...  
Keyword(s):  
Data Collection ◽  
Drug Administration ◽  
Financial Reporting ◽  
Mass Drug Administration ◽  
Lessons Learned ◽  
Secondary Outcome ◽  
Data Quality Control ◽  
Soil Transmitted Helminths ◽  
Trade Offs ◽  
Rich Data

ObjectiveTo present a costing study integrated within the DeWorm3 multi-country field trial of community-wide mass drug administration (cMDA) for elimination of soil-transmitted helminths.DesignTailored data collection instruments covering resource use, expenditure and operational details were developed for each site. These were populated alongside field activities by on-site staff. Data quality control and validation processes were established. Programmed routines were used to clean, standardise and analyse data to derive costs of cMDA and supportive activities.SettingField site and collaborating research institutions.Primary and secondary outcome measuresA strategy for costing interventions in parallel with field activities was discussed. Interim estimates of cMDA costs obtained with the strategy were presented for one of the trial sites.ResultsThe study demonstrated that it was both feasible and advantageous to collect data alongside field activities. Practical decisions on implementing the strategy and the trade-offs involved varied by site; trialists and local partners were key to tailoring data collection to the technical and operational realities in the field. The strategy capitalised on the established processes for routine financial reporting at sites, benefitted from high recall and gathered operational insight that facilitated interpretation of the estimates derived. The methodology produced granular costs that aligned with the literature and allowed exploration of relevant scenarios. In the first year of the trial, net of drugs, the incremental financial cost of extending deworming of school-aged children to the whole community in India site averaged US$1.14 (USD, 2018) per person per round. A hypothesised at-scale routine implementation scenario yielded a much lower estimate of US$0.11 per person treated per round.ConclusionsWe showed that costing interventions alongside field activities offers unique opportunities for collecting rich data to inform policy toward optimising health interventions and for facilitating transfer of economic evidence from the field to the programme.Trial registration numberNCT03014167; Pre-results.

Urban Search and Rescue with Anti-pheromone Robot Swarm architecture

2021 ◽  
Author(s):  
Ruben Martin Garcia ◽  
Daniel Hernandez de la Iglesia ◽  
Juan F. de Paz ◽  
Valderi R. Q. Leithardt ◽  
Gabriel Villarrubia
Keyword(s):  
Search And Rescue ◽  
Urban Search And Rescue ◽  
Robot Swarm

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

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