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Authoring Negotiation Content and Programming Simulated Patients

机译:创作谈判内容和编程模拟患者

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摘要

Competent negotiation with simulated patients can save expenses in medical education and improve outcomes for all parties involved. The use of simulated agents is beneficial for a study of human behaviour and cognition due to the possibility to create and manage a wide range of specific social situations. Building plausible cognitive models underlying the agent's intelligent behaviour from scratch is challenging and costly. Interaction designers and cognitive engineers require sufficient background knowledge to decide which domain information, resources and activities are important. Domain experts require sufficient understanding of human interaction and social cognition. All may lack advanced software development skills and an access to sufficient amount of authentic data. This paper presents a methodology to author cognitive agents and interactions with them. Authors can easily encode agents' knowledge and equip them with different sets of preferences and decision making strategies. This offers abundant opportunities for various social simulations: to create and control situations in which doctor's decision making and negotiation skills can be applied and assessed; employ and relate specific action patterns to various strategies and sociopragmatic variables of interactional power, social distance and degree of imposition; predict outcomes and explain why the choices made lead to what specific outcomes. The proposed approach also enables efficient collection of significant amount of annotated dialogue data and can be applied to model various medical and not medical negotiation scenarios.
机译:与模拟患者的主管谈判可以节省医学教育的费用,并改善所有参与各方的结果。由于可能创造和管理广泛的特定社交场合,使用模拟试剂的使用是有益的,对人类行为和认知有益。建立代理人智能行为的合理认知模型来自划痕的挑战性和昂贵。互动设计师和认知工程师需要充分的背景知识来确定哪个域信息,资源和活动很重要。领域专家需要足够了解人类互动和社会认知。所有这些都可能缺乏先进的软件开发技能和获得足够数量的真实数据。本文提出了作者认知代理和与他们的互动的方法。作者可以轻松地编码代理商的知识并用不同的偏好和决策策略装备它们。这为各种社交模拟提供了丰富的机会:创建和控制医生的决策和谈判技能的情况;雇用并将具体行动模式与互动权,社会距离和征收程度的各种策略和社会专业变量相关联;预测结果并解释为什么所做的选择导致特定结果。该方法还可以实现有效收集大量注释的对话数据,并可应用于模型各种医疗和不医疗谈判情景。

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