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An Agile Tool for Modeling Human Performance• CORE (Constraint-based Optimal
Reasoning Engine) introduces newflexibility in modeling and pre-dicting performance of humansinteracting with complex systems
• It’s easy for non-experts to use,while permitting experts to selectamong cognitive architectures andscheduling algorithms
Technology Spotlight
TechnologyCORE—Constraint-based OptimalReasoning Engine
FunctionEnables general users to model inter-active performance of humans usingnew computer/robotic interfaces
Relevant Missions• Human-Robotic Exploration
• International Space Station
• Phoenix 2007 Mars Mission
• Mars Science Laboratory
Features• “Guided-entry” interface that
prompts users for input of taskdescriptions
• Flexible choice of cognitive architec-tures and scheduling algorithms
• PERT chart-style graphical output
Benefits• Enables affordable, efficient design
and evaluation of complex interfaceconcepts without requiring workingprototypes
• Supports design of software toolsused in time-critical contexts
• Predicts performance for range of
users—novice to expert• Provides graphical report on time to
complete task, based on skill level,and probability of user error
• Measures speed/accuracy tradeoffs
• Quantifies and validates theories of cognition
Contacts• [email protected]
CORE enables almost anyone tomodel new human-machineinterfaces quickly and affordably.CORE automatically measures howlong it would take for humans(from novice to expert) to use theinterface to perform a task, as wellas the probability of errors. At leftis an artist’s rendition of anastronaut operating a drill on alunar exploratory mission.
The time it takes for an astronaut to per-form a task on a computer, in the cockpitof a spacecraft, or on a planet with arobot may mean the difference betweenthe mission’s success or failure, and evenlife or death. The system interfaces thatastronauts use are critical to how quickly,accurately, and safely they can do a job.
To develop and evaluate new system inter-faces for future space missions, scientistsat NASA are developing new, more agiletechnologies for modeling human perform-
ance. CICT’s Intelligent Systems (IS)Project is researching the design, develop-ment, and deployment of complex human-computer systems through its Human-Centered Computing subproject, managedby Michael Shafto.
Advancing human-centered computing“Our research teams are studying howhumans, software agents, and robots allcontribute to system behavior,” says
Shafto. “Therefore, we are looking at thecomputational tools, the cognitive andsocial systems, and the physical facilitiesand environments that best enable futureNASA missions to succeed.
“The interface is a critical component of human-centered systems,” says Shafto.“Until now, system interface design hasbeen the domain of specialists in humanperformance modeling, but those peopleare usually not experts in designing andexecuting actual NASA missions. We needto find an easier way to inject real missionexperience into interface design, and findmore affordable and accurate ways to pre-dict the efficacy of these interfaces withdifferent users. We don’t have the luxury of asking astronauts to spend valuable timetesting new interface designs until we knowthey are mission-ready. To meet this chal-lenge, Alonso Vera and his team at NASAAmes have developed the Constraint-basedOptimal Reasoning Engine, or CORE.”
Current challenges to modelingAlonso Vera, principal investigator for CORE, says, “Current methods for modelingand predicting human performance either require specialized training or are too rigidto easily accommodate different possibili-ties. Most other cognitive architecturesrequire that the modeler understand cogni-tive psychology and have sophisticated pro-gramming skills. Yet it can still take these
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I N FU S I N Computing, Information, andCommunications Technology Program
www.cict.nasa.goI n n o v a t i o n s f r o m t h e N A S A C I C T P r o g r a m
8/14/2019 NASA 044 CICT IS CORE A web
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Computing, Information, and Communications Technology Program
experts days to develop a model, whereaswe need to get it down to hours or evenminutes. And, even after days of expertdevelopment, these models cannot accom-modate multiple skill levels, different archi-tectural platforms, multiple interactive users,or display tradeoffs between speed andaccuracy. NASA needs a tool that is auto-
mated for use by novices, and is flexibleenough to provide more output options.”
Fast and easy modeling of performanceVera and his colleagues at NASA Ames,Irene Tollinger and Michael McCurdy, havedesigned CORE to simplify the process of modeling human performance for novicedesigners, while providing more options for experienced designers.
“CORE enables almost anyone to quicklymodel and predict human performance of asystem task,” says Vera. “CORE predicts task
times according to whether a single expertuser or multiple users of different capabili-ties are performing it. CORE can also pre-dict task performance given a user’s deci-sion on how he or she wants to trade speedfor accuracy, or vice versa.”
CORE’s X-PRT user interfaceIrene Tollinger, who is designing the X-PRTinterface for CORE, says, “X-PRT will enableeven novice users to effectively create,debug, and visually verify the performanceof their models. As a guided-entry inter-face, it assists the user in describing the
essential steps in the task without imposinga particular sequence on those steps. It’swell known that as users learn a task theybegin to interleave elements. For example,before finishing one step, they mightglance ahead at something they are goingto need in the next step. An expert user canperform the steps differently from a novice.The expert may interleave elements of twodifferent steps.” (See illustration above.)
The X-PRT interface enables even novices todescribe the task or set of tasks, the devices
used (mouse, keyboard, screen, etc.), and thecognitive strategy for achieving the task, suchas moving the mouse, or turning a knob, or clicking a button. The cognitive strategy canalso include slow and fast moves dependingon how precise a move must be.
CORE then applies embedded (but modifi-
able) rules and constraints to the behavioralelements of the task described, and gener-ates a graphical chart of the optimal timingand sequence of steps in the task (see illus-tration above). CORE also predicts theresource requirements and total time of theactivity (when the steps are done in a par-ticular order). A designer can then revise themodel to obtain better performance or adjust the tradeoff of speed vs. accuracy.
CORE provides flexibility and choiceA key feature of CORE is its flexibility. Thecognitive architectures used to model human
performance include implicit assumptions or rules about human performance. Take, for example, the simple task of clicking a mouseon a screen icon. Some cognitive architec-tures define the whole move as a single unitand proscribe the average time that will take.CORE, however, can break that move into itsconstituent “operators”—gaze, perception,cognition, and motor activity—and thusaccommodate alternative sequences of oper-ator events, which enables it to track andmeasure the benefits of expert interleaving.
CORE’s timing of the operators is based on
a cognitive architecture—in this case,Card, Moran, and Newell’s Model HumanProcessor—but, if expert designers wish,they can select another architecture fromthe CORE library, or even customize anarchitecture by re-defining the fundamentaloperators and resources, as well as their parameters and dependencies.
The key is constraint satisfactionVera, McCurdy and Tollinger argue that it isfaster and easier to predict performancefrom a set of architectural, strategic,
environmental, and task constraints. Theyused that approach to model a staff-sched-ule data entry task for the CollaborativeInformation Portal, which was used suc-cessfully on a daily basis by NASA person-
nel on the Mars Exploration Rover mission.
“By defining these constraints separatelyinstead of preemptively binding them toeach other,” says Vera’s colleague MichaeMcCurdy, “we allow more flexibility in howtheir relationships can be computed. We alsomaintain their independence from arbitraryconstraints imposed by the machine or thesoftware algorithms used to model the task.”
As a result, expert designers can mix andmatch cognitive architectures and descrip-tion languages, choosing from the options
in the CORE libraries.
“CORE is an agile tool,” says Vera. “It pro-vides novice designers with an easy-to-usemodeling interface and a clear, quantifiablereport on performance, while still enablingexpert designers to customize the archi-tecture to meet their own needs.”
–Larry Laufenberg
For more information or stories online, seewww.cict.nasa.gov/infusion
CORE produces a chart (left)that breaks the steps of a taskdown into its respective“operators,” showing how longeach takes (in milliseconds). Thischart shows how an expertbegins a second step (green)before the first (blue) iscomplete—called “interleaving.”
04-11-044-A-IS-CORE
NASA CICT Program OfficeAmes Research Center 650.604.2494
Program Manager Butler Hine
Deputy Program Manager Bill Van Dalsem
Infusion Editor
Larry [email protected]
www.cict.nasa.gov
CICT is part of the NASA Exploration Systems
Mission Directorate’s Human and Robotic
Technology Theme which represents NASA's com-
mitment to investing in the technologies and capa-
bilities that will make an ambitious and sustainable
21st century space exploration program possible.
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