When the Same Advisory Rule Produces Different Decisions
TL;DR for operators An AI agent’s proposed action, the decision to accept it, and the action eventually executed are three different events. A new shared-autonomy framework makes those stages explicit through reusable evaluation mechanisms while retaining standard Markov decision process (MDP) planning. The formal contribution is a structured representation of interaction rules, not a more expressive decision model. In a synthetic navigation task, changing human attention alters the optimal recommendation at one bridge approach, even though the interaction remains advisory. All 20 reported state-attention configurations show positive modeled marginal value for their reference recommendations relative to silence. ...