Intelligence and Natural Language Processing for Advanced Tactical
Decision-Making Immersive Training”, RTI International, $100,156,
Funded. (start: September 16, 2019,
end: June 24, 2021).
"Mixed Initiative Spoken Dialogue Control",
Honeywell, Inc., Private, $98,000.00, Awarded. (start: April 1, 2015, end:
March 30, 2016).
An Application of Responsive Virtual Human Technology, with Geoffrey Frank
and Robert Hubal, accepted for publication, Proceedings of the 24th
Interservice/Industry Training, Simulation and Education Conference, 2002. (htm) , (doc)
In this paper, we describe an application of
responsive virtual humans to train law enforcement personnel in dealing with
subjects that present symptoms of serious mental illness. JUST-TALK provides
a computerized virtual person to interact with the student in a role-playing
environment. Students were able to converse with the virtual person using
spoken natural language and see and hear the virtual human a combination of
facial gesture, body movements, and spoken language. The JUST-TALK project,
funded by the National Institute of Justice Office of Science and Technology
and developed by RTI International, involved integrating virtual reality
training software within a 3-day class at the North Carolina Justice Academy.
The course was structured to include classroom-based lecture, videos,
discussion, live human role-playing, and virtual human role-playing.
A scientific evaluation of the class and the software system was carried out
by North Carolina State University. This assessment investigated the
contribution of natural language interfaces and virtual reality technology to
learning in this applied setting. Results of the evaluation are extremely
encouraging. The vast majority of students (88 percent) found the simulation
easy to use. A majority of the students said the virtual trainer enhanced
their learning in the course. As a training tool, students rated the computer
simulation on par with other training methods including lecture, role-play and discussion. A total of 59 percent of
students felt the simulation was better for learning or comparable to
role-play; 77 percent felt simulation was better than or comparable to
lecture; and 59 percent felt the simulation was better than or comparable to
Test of Responsive Virtual Human Technology as an Interviewer Skills Training
Tool. With Link, M.W., Armsby, P. P., and Hubal, R.
Proceedings of the 2002 Annual Conference of the American Association for
Public Opinion Research , St. Petersburg. 2002. (htm) , (doc)
Research on survey
non-response suggests that advanced communication and listening skills are
among the best strategies telephone interviewers can employ for obtaining
survey participation, allowing them to identify and address respondents'
concerns immediately with appropriate, tailored language. Yet, training on
interaction skills is typically insufficient, relying on role-playing or passive
learning through lecture and videos. What is required is repetitive,
structured practice in a realistic work environment. This research examines
acceptance by trainees of an application based on responsive virtual human
technology (RVHT) as a tool for teaching refusal avoidance skills to
telephone interviewers. The application tested here allows interviewers to
practice confronting common objections offered by reluctant sample members.
Trainee acceptance of the training tool as a realistic simulation of
"real life" interviewing situations is the first phase in
evaluating the overall effectiveness of the RVHT approach. Data were gathered
from two sources -- structured debrief questionnaires administered to users
of the application, and observations of users by researchers and instructors.
The application was tested with a group of approximately fifty telephone
interviewers of varying skill and experience levels. The research presents
findings from these acceptance evaluations and discusses users' experiences
with and perceived effectiveness of the virtual training tool.
Virtual Standardized Patient: Simulated Patient-Practitioner Dialogue for
Patient Interview Training. With Hubal, R.C., Kizakevich,
P.N., Merino, K.D., & West, S.L. In J.D. Westwood, H.M. Hoffman, G.T. Mogel, R.A. Robb, & D. Stredney
(Eds.), Envisioning Healing: Interactive Technology and the
Patient-Practitioner Dialogue. IOS Press: Amsterdam, 2000. (htm) , (doc)
We describe the
Virtual Standardized Patient (VSP) application, having who interacts with medical
practitioners in much the same way as actors hired to teach and evaluate
patient assessment and interviewing skills. The VSP integrates technologies
from two successful research projects conducted at Research Triangle
provides natural language processing, emotion and behavior modeling, and
composite facial expression and lip-shape modeling for a natural
patient-practitioner dialogue. Trauma Patient Simulator (TPS) provides
case-based patient history and trauma casualty data, real-time physiological
modeling, interactive patient assessment, 3-D scenario simulation, and
instructional record-keeping capabilities. The VSP offers training benefits
that include enhanced adaptability, availability, and assessment.
Language Processing in Virtual Reality, with R. Jorge Montoya, Modern
Simulation and Training , pp. 44-55, June 1998. (htm) , (pdf)
advances in areas such as transportation, communications, and science are
rapidly changing our world--the rate of change will only increase in the 21st
century. Innovations in training will be needed to meet these new
requirements. Not only must soldiers and workers become proficient in using
these new technologies, but shrinking manpower requires more cross-training,
self-paced training, and distance learning. Two key technologies that can
help reduce the burden on instructors and increase the efficiency and
independence of trainees are virtual reality simulators and natural language
processing. This paper focuses on the design of a virtual reality trainer
that uses a spoken natural language interface with the trainee.
RTI has developed the Advanced Maintenance Assistant and Trainer (AMAT) with
ACT II funding for the Army Combat Service Support (CSS) Battlelab.
AMAT integrates spoken language processing, virtual reality, multimedia and instructional technologies to train and
assist the turret mechanic in diagnosing and maintenance on the M1A1 Abrams
Tank in a hands-busy, eyes-busy environment. AMAT is a technology concept
demonstration and an extension to RTI’s Virtual Maintenance Trainer (VMAT)
which was developed for training National Guard organizational mechanics.
VMAT is currently deployed in a number of National Guard training facilities.
The AMAT project demonstrates the integration of spoken human-machine
dialogue with visual virtual reality in implementing intelligent assistant
and training systems. To accomplish this goal, RTI researchers have
implemented the following features:
· Speech recognition on a
· Error correcting parsers that can correctly handle
utterances that are outside of the grammar,
· Dynamic natural language grammars that change as the
situation context changes,
· Spoken message interpretation that can resolve
pronoun usage and incomplete sentences,
· Spoken message reliability processing that allows
AMAT to compute the likelihood that it properly understood the trainee (This
score can be used to ask for repeats or confirmations.),
· Goal-driven dialogue behavior so that the computer is
directing the conversation to satisfy either the user-defined or
· Voice-activated movement in the virtual environment,
· Voice synthesis on a Pentium-based PC.
Multimedia Dialogue with Variable Initiative, with Alan W. Biermann, Michael
S. Fulkerson, Greg A. Keim, Zheng Liang, Douglas M.
Melamed, Krishnan Rajagopalan, in International Symposium on
Methodologies for Intelligent Systems, pp. 1-16, 1997.
offers several interesting challenges to mixed-initiative dialogue systems.
In this paper, we outline some distinctions between tutorial dialogues and
the more familiar task-oriented dialogues, and how these differences might
impact our ideas of focus and initiative. In order to ground discussion, we
describe our current dialogue system, the Duke Programming Tutor. Through
this system, we present a temperature-based model and algorithm which provide
a basis for making decisions about dialogue focus and initiative.
Collaborative Discourse: A Theory and its Implementation, with Alan Biermann,
D. Richard Hipp, and Ronnie Smith in ARPA
Workshop on Human Language Technology, Princeton, NJ, March 1993.
for voice dialogue machines is described with emphasis on the problem solving
and high level decision making mechanisms. The
architecture provides facilities for generating voice interactions aimed at
cooperative human-machine problem solving. It assumes that the dialogue will
consist of a series of local self-consistent subdialogues
each aimed at subgoals related to the overall task. The discourse may consist
of a set of such subdialogues with jumps from one subdialogue to the other in a search for a successful
conclusion. The architecture maintains a user model to assure that
interactions properly account for the level of competence of the user, and it
includes an ability for the machine to take the initiative or yield the
initiative to the user. It uses expectation from the dialogue processor to
aid in the correction of errors from the speech recognizer.