Balancing Performance and Human Autonomy with Implicit Guidance Agent

no code implementations • 1 Sep 2021 • Ryo Nakahashi, Seiji Yamada

We modeled a collaborative agent with implicit guidance by integrating the Bayesian Theory of Mind into existing collaborative-planning algorithms and demonstrated through a behavioral experiment that implicit guidance is effective for enabling humans to maintain a balance between improving their plans and retaining autonomy.

Modeling Human Inference of Others' Intentions in Complex Situations with Plan Predictability Bias

no code implementations • 16 May 2018 • Ryo Nakahashi, Seiji Yamada

However, perfect inverse planning differs from human cognition during one kind of complex tasks due to human bounded rationality.

Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model

no code implementations • 3 Dec 2015 • Ryo Nakahashi, Chris L. Baker, Joshua B. Tenenbaum

Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences.