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Learning the Task Management Space of an Aircraft Approach ModelValidating models of airspace operations is a particular challenge. These models are often aimed at finding and exploring safety violations, and aim to be accurate representations of real-world behavior. However, the rules governing the behavior are quite complex: nonlinear physics, operational modes, human behavior, and stochastic environmental concerns all determine the responses of the system. In this paper, we present a study on aircraft runway approaches as modeled in Georgia Tech's Work Models that Compute (WMC) simulation. We use a new learner, Genetic-Active Learning for Search-Based Software Engineering (GALE) to discover the Pareto frontiers defined by cognitive structures. These cognitive structures organize the prioritization and assignment of tasks of each pilot during approaches. We discuss the benefits of our approach, and also discuss future work necessary to enable uncertainty quantification.
Document ID
20140006942
Document Type
Conference Paper
Authors
Krall, Joseph (West Virginia Univ. WV, United States)
Menzies, Tim (West Virginia Univ. WV, United States)
Davies, Misty (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
June 9, 2014
Publication Date
March 1, 2014
Subject Category
Systems Analysis and Operations Research
Aircraft Design, Testing and Performance
Behavioral Sciences
Report/Patent Number
ARC-E-DAA-TN12925
Meeting Information
AAAI Modeling in Human-Machine Systems: Challenges for Formal Verification(Palo Alto, CA)
Funding Number(s)
WBS: WBS 534723.02.02.01.40
CONTRACT_GRANT: NSF CCF-1017330
CONTRACT_GRANT: NPRP 09-12-5-2-470
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
Validation
Optimization
Cognitive Modeling

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