Enabling distributed command and control with standards-based geospatial collaboration

Ciaccio, Ray Di; Pullen, Jared; Breimyer, Paul

doi:10.1109/ths.2011.6107921

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…One of the tools currently used by first-responders is the Next Generation Incident Command System (NICS) [52]. This command-and-control system allows a distributed team of responders to efficiently exchange information and coordinate mission planning.…”

Section: A Web-based Tool Designmentioning

confidence: 99%

Improving Team's Consistency of Understanding in Meetings

Kim

Shah

2016

IEEE Trans. Human-Mach. Syst.

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Abstract-Upon concluding a meeting, participants can occasionally leave with different understandings of what had been discussed. Detecting inconsistencies in understanding is a desired capability for an intelligent system designed to monitor meetings and provide feedback to spur stronger shared understanding.In this paper, we present a computational model for the automatic prediction of consistency among team members' understanding of their group's decisions. The model utilizes dialogue features focused on the dynamics of group decision-making. We trained a hidden Markov model using the AMI meeting corpus and achieved a prediction accuracy of 64.2%, as well as robustness across different meeting phases. We then implemented our model in an intelligent system that participated in human team planning about a hypothetical emergency response mission. The system suggested topics that the team would derive the most benefit from reviewing with one another. Through an experiment with 30 participants, we evaluated the utility of such a feedback system, and observed a statistically significant increase of 17.5% in objective measures of the teams' understanding compared with that obtained using a baseline interactive system. c 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Index Terms-Consistency of understanding, intelligent agent participation, adaptive review, human-computer interaction, dialogue acts, hidden Markov models.

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Section: A Web-based Tool Designmentioning

confidence: 99%

Improving Team's Consistency of Understanding in Meetings

Kim

Shah

2016

IEEE Trans. Human-Mach. Syst.

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“…Disaster response teams are increasingly utilizing webbased planning tools to plan their deployments (Di Ciaccio, Pullen, and Breimyer 2011). Dozens to hundreds of responders log in to plan their deployment using audio/video conferencing, text chat, and annotatable maps.…”

Section: Problem Formulationmentioning

confidence: 99%

“…We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. Human Team Planning Data We designed a web-based collaboration tool that is modeled after the NICS system (Di Ciaccio, Pullen, and Breimyer 2011) used by first response teams, but with a modification that requires the team to communicate soley via text chat. Twenty-three teams of two (total of 46 participants) were recruited through Amazon Mechanical Turk and the greater Boston area.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior

Kim

Chacha

Shah

2013

AAAI

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We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans. This hybrid approach enables us to overcome the challenge of performing inference over the large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentation and show we are able to infer a human team's final plan with 83% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work that integrates a logical planning technique within a generative model to perform plan inference.

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“…The Next-Generation Incident Command System (NICS) is a platform developed by the MIT Lincoln Lab (Bremyer, 2011;Di Ciaccio et al, 2011). Its main goal is to assist first responders by allowing them a better situational awareness and provide them with tools to collaborate and communicate during natural disasters.…”

Section: Introductionmentioning

confidence: 99%