The Human Operator Simulator: An Overview

Lane, Norman E.; Strieb, Melvin I; Glenn, Floyd; Wherry, Robert J.

doi:10.1007/978-1-4613-3306-7_5

Cited by 20 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adciibonaliy, the user can define and access Informaition on systemn measures of effoctiveness. Lane et al (1981) idlentiftoed a numb~er of appliations and validation efforts moer a widco range of systems. Generally, the results have been very favorable.…”

Section: Rswmatmentioning

confidence: 99%

Operator Workload: Comprehensive Review and Evaluation of Operator Workload Methodologies

Lysaght¹,

Hill²,

Dick³

et al. 1989

129

View full text Add to dashboard Cite

Section: Rswmatmentioning

confidence: 99%

Operator Workload: Comprehensive Review and Evaluation of Operator Workload Methodologies

Lysaght¹,

Hill²,

Dick³

et al. 1989

129

View full text Add to dashboard Cite

“…work (Glenn, Schwartz & Ross, 1992) and (Lane, Strieb, Glenn & Wherry, 1981). The term memory moderation was used to include various memory-related factors, such as memory load, decay, opportunities for rehearsal, and interference.…”

Section: Approach To Memory Moderationmentioning

confidence: 99%

“…The values for the parameters in the memory moderation algorithm were determined by using an initial value based on the Human Operator Simulator (HOS) work (Glenn et al, 1992;Lane et al, 1981), then adjusting those values based on some trial-and-error experimentation after the learning mechanism was integrated into the COGNET architecture. The value of T (the length of the inter-trial interval) was determined from the task environment simulation to be 50 sec.…”

Section: Memory Moderationmentioning

confidence: 99%

Developing Concept Learning Capabilities in the COGNET/IGEN Integrative architecture and Associated Agent-based Modeling and Behavioral Representation (AMBR) Air Traffic Control

Zachary¹,

Ryder²,

Stokes³

et al. 2004

View full text Add to dashboard Cite

Public reporting burden for this collection of infornation is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of informration. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY)2 SUPPLEMENTARY NOTES ABSTRACTThis report describes CHI Systems' efforts during Phase III and IV of the model comparison process of the Agent-based Modeling and Behavioral Representation (AMBR) program conducted by the Air Force Research Laboratory. Phases III and IV focused on modeling and simulating concept learning within the context of a simulated Air Traffic Control (ATC) work environment.CHI Systems extended the COGNET/iGEN framework used in prior AMBR phases, implementing a general capability to learn the conditions under which each of a disjunctive set of goals or actions should be taken. Deeper extensions to the system enabled the representation of memory decay, rehearsal, and proactive interference needed to model human learning performance.The Phase III and IV COGNET/iGEN model execution results were compared with the results of three other models (ACT-R, D-Cog, EPIC-Soar) and results collected from human trials.On most measures (accuracy, response time, workload) iGEN model results were indistinguishable from human performance and, overall, iGEN results provided a better fit to the human data than the other models tested.

show abstract

“…To permit this in a granularity flexible manner (in which there would be no fixed models of body features), the performance prediction approach of micromodels was adopted. This approach, which originated with the Human Operator Simulator (HOS) system of the 1970s (Lane, Strieb, Glenn, and Wherry, 1981), uses closed form approximations of the time and/or accuracy constraints of specific physical mstrumentalities in specific types of contexts (e.g., accuracy of reading characters of text; time to fingertip-touch an object within the current reach envelope, etc.). These micromodels allow existing experimental data and empirical relationships to be encapsulated and reused, but do not force any specific level of representation of body features in CGF-COGNET.…”

Section: Background and Foundationmentioning

confidence: 99%

Developing a Multi-Tasking Cognitive Agent Using the COGNET/iGEN Integrative Architecture

Zachary¹,

Santarelli²,

Ryder³

et al. 2000

View full text Add to dashboard Cite

REPORT DOCUMENTATION PAGE FormAppnved 0MB No. 0704-0188PiWfc npsrts^ bunlen its this ei^lectisn of mfsrmatran Is sstmsted Is ssrsags 1 how per rs^jonse^ Induifing the tmo for roviBwIi^ Instnictisis, asrehmj oxistlngdataseiiress, gsAsraq ant! raamtaWnfl Ihs data r»eded, aod ejnv^ling and resswng tha sofeotion off infsmstien. Send eaimants ragardiiq tins inirtto estniata or any otfiar ablest off tNs oi^stion of infonnation, mcludfl^ siqgestlsns for rsduEwig Mris burden, to Wa^ungton Headquarters Servicss, Onotorate far Infermatnn OperetlMi! wd Reports, 1216 Jefferson Dews H^liway, Suite H04, Mnjton, W 222024302, end to tte Offflce of Manajen«nt aid Bydjet, P^rwwk Raitetien ftojett B)704-0ieB|, Wasliintoii, DC 2M03.

show abstract

The Human Operator Simulator: An Overview

Cited by 20 publications

References 10 publications

Operator Workload: Comprehensive Review and Evaluation of Operator Workload Methodologies

Operator Workload: Comprehensive Review and Evaluation of Operator Workload Methodologies

Developing Concept Learning Capabilities in the COGNET/IGEN Integrative architecture and Associated Agent-based Modeling and Behavioral Representation (AMBR) Air Traffic Control

Developing a Multi-Tasking Cognitive Agent Using the COGNET/iGEN Integrative Architecture

Contact Info

Product

Resources

About