From Proposal to Execution: Modeling Interaction Semantics in Shared Autonomy

Key points

• In many human-AI systems, candidate actions are not executed directly but are first evaluated by another decision-making entity under a potentially different objective or decision model.1
• We formalize this through a joint action language and an interpretation function that maps each joint action and context to a semantic outcome.1
• Transition and reward dynamics are induced through these semantic outcomes, thereby introducing an explicit representational layer for interaction semantics.1
• We instantiate the framework in shared human-AI autonomy, capturing interaction patterns such as advisory and authorization, and illustrating how interaction context influences decision making.1

Sources (1)

• [1]From Proposal to Execution: Modeling Interaction Semantics in Shared Autonomy
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 07:34 PM

  We introduce a semantics-aware decision framework in which a candidate action is proposed, evaluated under an interaction context, and then resolved into an executed outcome.

  In many human-AI systems, candidate actions are not executed directly but are first evaluated by another decision-making entity under a potentially different objective or decision model.

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