Agents providing assistance to humans are faced with the challenge of automatically adjusting the level of assistance to ensure optimal performance. In this work, we argue that identifying the right level of assistance consists in balancing positive assistance outcomes and some (domain-dependent) measure of cost associated with assistive actions. Towards this goal, we contribute a general mathematical framework for structured tasks where an agent playing the role of a ‘provider’—e.g., therapist, teacher—assists a human ‘receiver’—e.g., patient, student. We specifically consider tasks where the provider agent needs to plan a sequence of actions over a fixed time horizon, where actions are organized along a hierarchy with increasing success probabilities, and some associated costs. The goal of the provider is to achieve a success with the lowest expected cost possible. We present OAssistMe, an algorithm that generates cost-optimal action sequences given the action parameters, and investigate several extensions of it, motivated by different potential application domains. We provide an analysis of the algorithms, including proofs for a number of properties of optimal solutions that, we show, align with typical human provider strategies. Finally, we instantiate our theoretical framework in the context of robot-assisted therapy tasks for children with Autism Spectrum Disorder (ASD). In this context, we present methods for determining action parameters based on a survey of domain experts and real child-robot interaction data. Our contributions unlock increased levels of flexibility for agents introduced in a variety of assistive contexts.

中文翻译：

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固定视野任务中辅助人员的最佳动作序列生成

为人类提供帮助的代理商面临着自动调整帮助级别以确保最佳性能的挑战。在这项工作中，我们认为确定正确的援助水平在于平衡积极的援助成果和与援助行动相关的某些（与领域相关）成本的措施。为了实现这一目标，我们为结构化任务提供了一个通用的数学框架，在该结构中，扮演“提供者”（例如，治疗师，老师）角色的代理人辅助了“接收者”（例如患者，学生）的角色。我们专门考虑以下任务：提供商代理需要在固定的时间范围内计划一系列操作，这些操作是按照层次结构进行组织的，且成功概率不断增加，并且涉及一些相关成本。提供者的目标是以尽可能低的预期成本获得成功。我们介绍了OAssistMe，这是一种在给定操作参数的情况下生成成本最优操作序列的算法，并研究它的几个扩展，这些扩展是由不同的潜在应用程序域引起的。我们提供了对算法的分析，包括证明了最佳解决方案的许多属性的证明，这些证明与典型的人工提供者策略保持一致。最后，我们在针对儿童自闭症谱系障碍（ASD）的机器人辅助治疗任务中实例化了我们的理论框架。在这种情况下，我们介绍了根据领域专家和真实的儿童机器人交互数据调查确定行动参数的方法。