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dc.contributor.authorYildiz, Y.en_US
dc.contributor.authorAgogino, A.en_US
dc.contributor.authorBrat, G.en_US
dc.date.accessioned2015/07/28en_US
dc.date.accessioned2015-07-28T12:01:54Z
dc.date.available2015-07-28T12:01:54Z
dc.date.issued2014-05-08en_US
dc.identifier.citationYildiz, Y., Agogino, A., & Brat, G. (2014). Predicting Pilot Behavior in Medium-Scale Scenarios Using Game Theory and Reinforcement Learning. Journal of Guidance, Control, and Dynamics, 37(4), 1335-1343.en_US
dc.identifier.issn0731-5090 (Print)en_US
dc.identifier.urihttp://hdl.handle.net/11693/12556
dc.descriptionCataloged from PDF version of article.en_US
dc.description.abstractA key element to meet the continuing growth in air traffic is the increased use of automation. Decision support systems, computer-based information acquisition, trajectory planning systems, high-level graphic display systems, and all advisory systems are considered to be automation components related to next generation (NextGen) air space. Given a set of goals represented as reward functions, the actions of the players may be predicted. However, several challenges need to be overcome. First, determining how a player can attempt to maximize their reward function can be a difficult inverse problem. Second, players may not be able to perfectly maximize their reward functions. ADS-B technology can provide pilots the information, position, velocity, etc. of other aircraft. However, a pilot has limited ability to use all this information for his/her decision making. For this scenario, the authors model these pilot limitations by assuming that pilots can observe a limited section of the grid in front of them.en_US
dc.language.isoen_USen_US
dc.source.titleJournal of Guidance, Control and Dynamicsen_US
dc.relation.isversionofhttp://dx.doi.org/10.2514/1.G000176en_US
dc.rightsCopyright © 2015 AIAAen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectDecision Support Systemsen_US
dc.subjectDisplay Devicesen_US
dc.subjectInverse Problemsen_US
dc.subjectReinforcement Learning Advisory Systemsen_US
dc.subjectAutomation Componentsen_US
dc.subjectHigh-level Graphicsen_US
dc.subjectInformation Acquisitionsen_US
dc.subjectMedium-scaleen_US
dc.subjectPilot Behavioren_US
dc.subjectReward Functionen_US
dc.subjectTrajectory Planningen_US
dc.titlePredicting pilot behavior in medium-scale scenarios using game theory and reinforcement learningen_US
dc.typeResearch Paperen_US
dc.departmentDepartment of Mechanical Engineeringen_US
dc.citation.spage1335en_US
dc.citation.epage1342en_US
dc.citation.volumeNumber37en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.2514/1.G000176en_US
dc.publisherAIAAen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US


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