Thinking About, and Interacting with Living and Mechanical Agents

人工智能和机器人技术的最新进展使各个年龄段的人都面临着一系列无视类别的实体，这些实体结合了生物和非生物的特征。因此，这些实体日益挑战人们对心灵和智力的基本理解。该项目的目标是探索成人和儿童对一系列生物和机械代理的信念，并测试这些信念如何影响人们在两个特定的计算机界面中跟踪、记忆和理解机械代理的能力。首先，它将探索设计一个计算机界面，允许人类操作员与一组半自动机器人进行交互和控制。第二个环境将是一个可教的代理系统，在这个系统中，中学生通过积极地教授一个动画软件代理来学习复杂的科学现象，比如河流生态系统。这个项目是为数不多的几个以经验检验人们对活体和人工代理的理解的研究项目之一，它将采用一个概念框架，从naïve对心灵的理解开始。“心智理论”)，并将它们应用到使用这些理解的工程环境中。这个框架描述了参与者应用不同代理概念的条件，可以帮助理解当人们与新代理互动时，这些信念如何随着时间的推移而变化。虽然这个框架还不是一个完整的理论，但它代表了一种超越现有心智理论双过程模型的扩展方法，可以用来推理典型的和新颖的生物和机械代理。这些实验还建立了智能体概念之间的联系，以及这些概念在现实高负载感知任务中的部署，因此它们可以为我们对知识如何影响视觉的基本理解做出重要贡献。这个项目的发现可能对教育儿童和成人处理新的智能决策技术具有重要意义，这些技术超越了当前大多数计算机应用程序固有的简单命令和响应周期。pi先前的研究已经记录了不同的人对这些技术的不同方法（例如，老年人和年轻人似乎对计算机智能的本质有微妙的不同看法），所以这个项目可能有助于提高新代理技术对不同人群的可访问性。更一般地说，由于本研究使用交互式教育工具和现实机器人命令系统来探索代理理解，它有可能改善用户界面，支持专注于自我调节学习的社会学习环境，并促进人机应急响应团队的有效性。这些技术挑战了用户对智力和思维的最基本理解，我们的研究有可能指导儿童和成人成为未来互动技术的成功用户和创造者。

Recent advances in artificial intelligence and robotics are confronting individuals of all ages with a series of category-defying entities that combine features of living and nonliving things. As such, these entities increasingly challenge people's basic understanding of mind and intelligence. The goal in this project is to explore adults' and children's beliefs about a range of living and mechanical agents, and to test how these beliefs affect people's ability to track, remember, and understand mechanical agents in two specific computer interfaces. First, it will explore a computer interface designed to allow a human operator to interact with and control a set of semi-autonomous robots. The second environment will be a teachable agent system in which middle school children learn about complex science phenomena, such as river ecosystems, by actively teaching an animated software agent.This project represents one of the few research programs to empirically test people's understanding of living and artificial agents, and it will employ a conceptual framework that starts with naïve understandings of mind (e.g. "Theory of Mind") and applies them to engineered environments where these understandings are used. This framework describes the conditions under which participants apply different agent concepts, and can help understand how these beliefs might change over time as people interact with novel agents. Although the framework is not yet a complete theory, it represents a broadened approach to reasoning about both typical and novel living and mechanical agents that goes beyond existing dual-process models of Theory of Mind. These experiments also make links between concepts about agents, and the deployment of these concepts in realistic high-load perceptual tasks, so they can make an important contribution to our basic understanding of how knowledge affects vision.The findings from this project may have important implications for educating both children and adults to deal with novel intelligent decision making technologies that move beyond the simple command-and-response cycle inherent to most current computer applications. Previous research by the PIs has already documented ways in which different people vary in their approach to these technologies (e.g. older and younger adults seem to have subtly different beliefs about the nature of computer intelligence), so this project may help improve the accessibility of novel agent-technologies to a wide range of different populations. More generally, because this research uses interactive educational tools and realistic robot-command systems to explore agent-understanding, it has the potential to improve user interfaces supporting social learning environments that focus on self-regulated learning, and that facilitate the effectiveness of human-machine emergency response teams. These technologies confront users with challenges to their most basic understandings of intelligence and thinking, and our research has the potential to guide both children and adults as they become successful users and creators of the interactive technologies of the future.