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DTSTART;TZID=America/New_York:20221013T133000
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UID:HFESAM_HFES 66th International Annual Meeting_sess176_LECT108@linkling
 s.com
SUMMARY:Modeling Goal Alignment in Human-AI Teaming:  A Dynamic Game Theor
 y Approach
DESCRIPTION:Lecture\n\nModeling Goal Alignment in Human-AI Teaming:  A Dyn
 amic Game Theory Approach\n\nli, Lee\n\nAs human-AI teaming becomes increa
 singly prevalent, goal alignment has emerged as a critical yet\nunsolved i
 ssue. Misaligned goals can be amplified by the situation and strategic int
 eractions, which can\nfurther impact the teaming process and performance. 
 These interrelated factors lack a systematic and\ncomputational model. To 
 address this gap, we developed a dynamic game theoretical framework simula
 ting the human-AI interdependency by integrating the Drift Diffusion Model
  simulating the goal alignment process. A 3 (Situation Structure) × 3 (Str
 ategic Behaviors) × 2 (Initial Goal Alignment) simulation study of human-A
 I teaming was designed. Results showed that teaming with an altruistic age
 nt in a competitive situation leads to the highest team performance. Moreo
 ver, goal alignment process can dissolve the initial goal conflict. Our st
 udy provides a first step of modeling goal alignment and implies a tradeof
 f between a balanced and cooperative team to guide human-AI teaming design
 .\n\nTrack: Human AI Robot Teaming (HART), Human Performance Modeling
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