Shashank Mehrotra — accepted AI-conference papers

2026

Strategic Shaping of Human Prosociality: A Latent-State POMDP Framework

Zahra Zahedi, Xinyue Hu, Shashank Mehrotra, Mark Steyvers, Kumar Akash

RA-L 2026

We propose a decision-theoretic framework in which a robot strategically can shape inferred human's prosocial state during repeated interactions. Modeling the human's prosociality as a latent state that evolves over time, the robot learns to infer and influence this state through its own actions, in

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2025

Toward Informed AV Decision-Making: Computational Model of Well-being and Trust in Mobility

Zahra Zahedi, Shashank Mehrotra, Teruhisa Misu, Kumar Akash

IJCAI 2025

For future human-autonomous vehicle (AV) interactions to be effective and smooth, human-aware systems that analyze and align human needs with automation decisions are essential. Achieving this requires systems that account for human cognitive states. We present a novel computational model in the for

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