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    Modeling Human Learning in Games

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    Name:
    NorahAlghamdi_MS_Thesis.pdf
    Size:
    2.766Mb
    Format:
    PDF
    Description:
    Final Thesis
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    Type
    Thesis
    Authors
    Alghamdi, Norah K. cc
    Advisors
    Shamma, Jeff S. cc
    Committee members
    Feron, Eric
    Laleg-Kirati, Taous-Meriem cc
    Program
    Electrical Engineering
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-12
    Permanent link to this record
    http://hdl.handle.net/10754/666444
    
    Metadata
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    Abstract
    Human-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.
    Citation
    Alghamdi, N. K. (2020). Modeling Human Learning in Games. KAUST Research Repository. https://doi.org/10.25781/KAUST-D029O
    DOI
    10.25781/KAUST-D029O
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-D029O
    Scopus Count
    Collections
    Theses; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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