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dc.contributor.advisorShamma, Jeff S.
dc.contributor.authorAlghamdi, Norah K.
dc.date.accessioned2020-12-17T08:48:15Z
dc.date.available2020-12-17T08:48:15Z
dc.date.issued2020-12
dc.identifier.citationAlghamdi, N. K. (2020). Modeling Human Learning in Games. KAUST Research Repository. https://doi.org/10.25781/KAUST-D029O
dc.identifier.doi10.25781/KAUST-D029O
dc.identifier.urihttp://hdl.handle.net/10754/666444
dc.description.abstractHuman-robot interaction is an important and broad area of study. To achieve success- ful interaction, we have to study human decision making rules. This work investigates human learning rules in games with the presence of intelligent decision makers. Par- ticularly, we analyze human behavior in a congestion game. The game models traffic in a simple scenario where multiple vehicles share two roads. Ten vehicles are con- trolled by the human player, where they decide on how to distribute their vehicles on the two roads. There are hundred simulated players each controlling one vehicle. The game is repeated for many rounds, allowing the players to adapt and formulate a strategy, and after each round, the cost of the roads and visual assistance is shown to the human player. The goal of all players is to minimize the total congestion experienced by the vehicles they control. In order to demonstrate our results, we first built a human player simulator using Fictitious play and Regret Matching algorithms. Then, we showed the passivity property of these algorithms after adjusting the passivity condition to suit discrete time formulation. Next, we conducted the experiment online to allow players to participate. A similar analysis was done on the data collected, to study the passivity of the human decision making rule. We observe different performances with different types of virtual players. However, in all cases, the human decision rule satisfied the passivity condition. This result implies that human behavior can be modeled as passive, and systems can be designed to use these results to influence human behavior and reach desirable outcomes.
dc.language.isoen
dc.subjectHuman Modeling
dc.subjectGame Theory
dc.subjectLearning
dc.subjectHuman Robot Interaction
dc.subjectPassivity
dc.titleModeling Human Learning in Games
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberFeron, Eric
dc.contributor.committeememberLaleg-Kirati, Taous-Meriem
thesis.degree.disciplineElectrical Engineering
thesis.degree.nameMaster of Science
refterms.dateFOA2020-12-17T08:48:16Z
kaust.request.doiyes


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