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Introduces CANOE, a multi-agent neuro-symbolic framework for open-ended care plan coordination that uses argumentative computation and human-in-the-loop contestation to improve transparency, safety, and clinical correctness.
This paper presents CoPlan, a co-intelligent and contestable interface for human-AI care planning that uses a multi-agent workflow to generate candidate interventions and arguments, allowing human care planners to inspect, challenge, and revise recommendations before final plan generation. It demonstrates the approach in an aging-in-place scenario and contributes a design framing for trustworthy human-AI care planning.