&lt;title&gt;Improving information assimilation by modeling warfighter context&lt;/title&gt;

Zaientz, Jack; Wood, Scott D.; Hawkins, Robert L.

doi:10.1117/12.592432

Cited by 2 publications

(4 citation statements)

References 17 publications

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“…The AAM provides the adaptation and customization necessary to facilitate mixed-initiative interaction between a soldier and unmanned assets. At present, we are working on combining the AAM with a plan execution system (Beard et al, 2003;Wood, 2003), and effective adaptive interaction techniques, based on the work in (Zaientz et al, 2005). While is still not clear what form these interfaces and intelligence must ultimately take to be optimal, ICF is addressing the technology and underlying scientific principles that will enable design exploration towards the creation of highly effective human-robotic teams.…”

Section: Discussion and Future Workmentioning

confidence: 96%

“…Aspects of the work behind the UI efforts in ICF can be found in (Zaientz et al, 2005). The PES in ICF has been discussed in (Beard et al, 2003;Wood, 2003), with the addition in ICF of more complete implementation of the deontic reasoning.…”

Section: Icf Architecturementioning

confidence: 98%

“…We initiated this research with the development of two systems for intelligent human-system interaction: one for task automation, called CIANC 3 (Beard et al, 2003;Wood, 2003), and one for enhanced information delivery, called BINAH (Zaientz et al, 2005). Each of these systems represents a core technology in ICF, but neither will be fully functional without some form of behavior-based adaptation or customization.…”

Section: Introductionmentioning

confidence: 99%

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Integrating adjustable autonomy in an intelligent control framework

DeKoven¹,

Wood²

2005

SPIE Proceedings

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Currently, multiple humans are needed to operate a single uninhabited aerial vehicle (UAV). In the near future, combat techniques will involve single operators controlling multiple uninhabited ground and air vehicles. This situation creates both technological hurdles as well as interaction design challenges that must be addressed to support future fighters. In particular, the system will need to negotiate with the operator about proper task delegation, keeping the operator appropriately apprised of autonomous actions. This in turn implies that the system must know what the user is doing, what needs to be done in the present situation, and the comparative strengths for of the human and the system in each task. Towards building such systems, we are working on an Intelligent Control Framework (ICF) that provides a layer of intelligence to support future warfighters in complex task environments. The present paper presents the Adjustable Autonomy Module (AAM) in ICF. The AAM encapsulates some capabilities for user plan recognition, situation reasoning, and authority delegation control. The AAM has the knowledge necessary to support operator-system dialogue about autonomy changes, and it also provides the system with the ability to act on this knowledge. Combined with careful interaction design, planning and plan-execution capabilities, the AAM enables future design and development of effective human-robot teams.

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Section: Discussion and Future Workmentioning

confidence: 96%

Section: Icf Architecturementioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Integrating adjustable autonomy in an intelligent control framework

DeKoven¹,

Wood²

2005

SPIE Proceedings

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“…Adapting system operating parameters to support operator needs requires the current operator state model (and current operating system parameters) to be used as input to determine the parameter set most advantageous to the operator. Adaptation options include alterations to almost any system functionality including altering the dialogue structure [25,38] or training method [3], proactive task completion [16], and information management [9,11,42]. In the case of operator control of unmanned systems, adaptation options may include switching UV control between operating modes (e.g.…”

Section: User Modeling Provides Adaptive Controlmentioning

confidence: 99%

Using GOMS and Bayesian plan recognition to develop recognition models of operator behavior

Zaientz¹,

DeKoven²,

Piegdon³

et al. 2006

SPIE Proceedings

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Trends in combat technology research point to an increasing role for uninhabited vehicles in modern warfare tactics. To support increased span of control over these vehicles human responsibilities need to be transformed from tedious, errorprone and cognition intensive operations into tasks that are more supervisory and manageable, even under intensely stressful conditions. The goal is to move away from only supporting human command of low-level system functions to intention-level human-system dialogue about the operator's tasks and situation.A critical element of this process is developing the means to identify when human operators need automated assistance and to identify what assistance they need. Toward this goal, we are developing an unmanned vehicle operator task recognition system that combines work in human behavior modeling and Bayesian plan recognition. Traditionally, human behavior models have been considered generative, meaning they describe all possible valid behaviors. Basing behavior recognition on models designed for behavior generation can offers advantages in improved model fidelity and reuse. It is not clear, however, how to reconcile the structural differences between behavior recognition and behavior modeling approaches.Our current work demonstrates that by pairing a cognitive psychology derived human behavior modeling approach, GOMS, with a Bayesian plan recognition engine, ASPRN, we can translate a behavior generation model into a recognition model. We will discuss the implications for using human performance models in this manner as well as suggest how this kind of modeling may be used to support the real-time control of multiple, uninhabited battlefield vehicles and other semi-autonomous systems.

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<title>Improving information assimilation by modeling warfighter context</title>

Cited by 2 publications

References 17 publications

Integrating adjustable autonomy in an intelligent control framework

Integrating adjustable autonomy in an intelligent control framework

Using GOMS and Bayesian plan recognition to develop recognition models of operator behavior

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