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The project focused on task and cognitive modeling to understand how technology affects organizations and key individuals, guided by cognitively inspired requirements. Software agents were intended to improve decision making, information filtering, distribution, integration, and overall team situational awareness. Substantial progress was made in both modeling infrastructure and task infrastructure; further work continues under a new 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was the main goal of the research contract?","Question",{"text":109,"@type":110},"To design a next-generation collaborative system that supports human experts with intelligent agents through multidisciplinary research across CMU and NASA.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"How did the project enhance team performance and situational awareness?",{"text":114,"@type":110},"By using task and cognitive modeling to inform cognitively inspired requirements, then developing software agents for decision support and for information filtering, distribution, and integration.",{"name":116,"@type":107,"acceptedAnswer":117},"What progress was reported in the final report period?",{"text":118,"@type":110},"Substantial progress was achieved in modeling infrastructure and task infrastructure, including advances toward making cognitive modeling more accessible and tractable, and continued development of MORSE for team performance and agent 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\nCarnegie Mellon University5000 Forbes AvenuePittsburgh,PA 15213  \nPrincipal investigator:Bonnie John  \nbej@cs.cmu.edu  \n(412)268-4469  \n(412)268-8825  \nCo-Investigator:  \nkatia@cs.cmu.edu  \nKatia Sycara  \n## 1.Abstract\n\nThe purpose of this research contract was to perform multidisciplinary researchbetween CMU psychologists,computer scientists and engineers and NASA researchersto design a next generation collaborative system to support a team of human expertsand intelligent agents.To achieve robust performance enhancement of such a system,we had proposed to perform task and cognitive modeling to thoroughly understand theimpact technology makes on the organization and on key individual personnel.Guidedby cognitively-inspired requirements,we would then develop software agents thatsupport the human team in decision making,information filtering,information distributionand integration to enhance team situational awareness.During the period covered bythis final report,we made substantial progress in modeling infrastructure and taskinfrastructure.Work is continuing under a different contract to complete empirical datacollection,cognitive modeling,and the building of software agents to support the teams'task.  \n## 1 Cognitive Modeling Infrastructure\n\nCognitive modeling is a potentially powerful tool for predicting human interactionwith computer systems(Pew and Mavor,1998),but it has historically been a difficultand time-consuming task,requiring researchers with doctorates in psychology and/orcomputer science.As part of this research contract,we made substantial progresstowards making cognitive modeling more accessible and tractable for modeling theknowledge required to operate complex systems.  \nOur first effort was to implement a well-established modeling framework,calledCPM-GOMS (John,1990),in a cognitive architecture developed at NASA,called Apex(Freed,1998).CPM-GOMS is a modeling method that combines the task  \ndecomposition of a GOMS analysis with a model of human resource usage at the levelof cognitive,perceptual,and motor operations.CPM-GOMS models have madeaccurate predictions about skilled user behavior in routine tasks,but developing suchmodels is tedious and error-prone.In the process of implementing CPM-GOMS in Apex,we discovered ways to automate some of the harder aspects of CPM-GOMS modeling.This breakthrough is documented in a CHI 2002 paper (John,Vera,Matessa,Freed,&Remington,2002,reproduced in Appendix A)where we describe the process forautomatically generating CPM-GOMS models from a hierarchical task decompositionexpressed in Apex.Resource scheduling in Apex automates the difficult task ofinterleaving the cognitive,perceptual,and motor resources underlying common taskoperators(e.g.mouse move-and-click).Apex's UI automatically generates PERTcharts,which allow modelers to visualize a model's complex parallel behavior.Becauseinterleaving and visualization is now automated,it is feasible to construct arbitrarily longsequences of behavior  \nWe further refined the theoretical ideas underlying our success in automating CPM-GOMS in a paper at the Cognitive Science Conference(Matessa,Vera,John,  \nRemington &Freed,2002,reproduced in Appendix B).In it,we explored the concept ofreusable templates of common behaviors and their efficacy for generating zero-  \nparameter a priori predictions of complex human behavior.This paper detailed thefeatures we believe are important when moving from hand-crafted models of particulartasks to reusable building blocks of commonly occurring behavior.As this becomescommon practice,proportionately more attention can be paid to the task analysisspecific to each new domain.We also taught a tutorial at the Cognitive ScienceConference about how to use Apex for different types of predictive cognitive modeling:KLM,GOMS and CPM-GOMS(Remington,John,Vera,Matessa,Freed,Dalal,Harris &Dahlman,2002).","cbCaijXnPbp40Wob","https://ap.wps.com/l/cbCaijXnPbp40Wob","pdf",718966,"English","# 1.Abstract\n## 1 Cognitive Modeling Infrastructure\n## 2 Task Infrastructure","[{\"question\":\"What was the main goal of the research contract?\",\"answer\":\"To design a next-generation collaborative system that supports human experts with intelligent agents through multidisciplinary research across CMU and NASA.\"},{\"question\":\"How did the project enhance team performance and situational awareness?\",\"answer\":\"By using task and cognitive modeling to inform cognitively inspired requirements, then developing software agents for decision support and for information filtering, distribution, and integration.\"},{\"question\":\"What progress was reported in the final report period?\",\"answer\":\"Substantial progress was achieved in modeling infrastructure and task infrastructure, including advances toward making cognitive modeling more accessible and tractable, and continued development of MORSE for team performance and agent research.\"}]","Final Report - Effective Team Support: From Modeling to Software Agents | PDF",1789147306]