| タイトル | Learning the Task Management Space of an Aircraft Approach Model |
| 本文（外部サイト） | http://hdl.handle.net/2060/20140006942 |
| 著者（英） | Davies, Misty; Krall, Joseph; Menzies, Tim |
| 著者所属（英） | NASA Ames Research Center |
| 発行日 | 2014-03-01 |
| 言語 | eng |
| 内容記述 | Validating 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. |
| NASA分類 | Systems Analysis and Operations Research; Aircraft Design, Testing and Performance; Behavioral Sciences |
| レポートNO | ARC-E-DAA-TN12925 |
| 権利 | Copyright, Distribution as joint owner in the copyright |
| URI | https://repository.exst.jaxa.jp/dspace/handle/a-is/80402 |
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