SBIR Phase II: A virtual role-playing simulation for social emotional learning using artificially intelligent characters and crowdsourcing

SBIR 第二阶段：使用人工智能角色和众包进行社交情感学习的虚拟角色扮演模拟

• 批准号: 1456093
• 负责人: Dan Tomaschko
• 金额: $ 75万
• 依托单位: Giant Otter Technologies, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-03-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1456093&HistoricalAwards=false
• 关键词: SBIR; Phase; II; virtual; role

This SBIR Phase II project will develop novel 3D web and mobile role-playing simulations that can improve social skills in children. Virtual characters participate in nuanced conversations with students, powering a transformational experience for practicing and assessing communication skills. Rigorous evaluations have demonstrated the feasibility and efficacy of teaching perspective-taking and social skills through simulated role-playing, beneficial to students struggling with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), or bullying. These simulations will disrupt the multi-billion dollar market for social emotional learning (SEL) by allowing students to learn and practice social skills as they would with teachers or therapists, but at a fraction of the cost. Current solutions for practicing and assessing social skills rely on face-to-face interaction, which is costly and difficult to scale. Producing socially rich, open-ended simulations has previously been infeasible, due to technical challenges required to replicate the complexity of human language. This project overcomes these barriers using an innovative technology with the potential to revolutionize how people learn and practice social skills by delivering real-time personalized feedback at scale. Future applications of the social behavior capture (So-Cap) technology that underlies this project range from contextually aware intelligent personal assistants to socially aware robotics. This project's unprecedented ability to respond authentically to open-ended natural language input relies on an innovative crowdsourced approach to artificial intelligence (AI), which imbues machines with the ability to understand dialogue in context and engage in extended conversations covering multiple topics. Individualized responses are tailored to user input by drawing from a massive database of recorded human dialogue, captured from online role-playing. The process for mining meaningful patterns from this data also employs crowdsourcing, relying on non-experts hired online to cluster and annotate words, utterances, and events. A computational model formed by these patterns powers a real-time conversational engine, which selects responses by combining AI techniques including plan recognition and case-based planning. The goal of this research is to pioneer a practical, repeatable process for democratizing the production of social simulations, minimizing cost while maximizing societal impact by providing social skills practice at scale.

这个SBIR第二阶段项目将开发新颖的3D网络和移动的角色扮演模拟，可以提高儿童的社交技能。虚拟角色参与与学生的细致入微的对话，为练习和评估沟通技能提供转型体验。 严格的评估已经证明了通过模拟角色扮演来教授观点采择和社交技能的可行性和有效性，这对患有自闭症谱系障碍（ASD），注意力缺陷多动障碍（ADHD）或欺凌的学生有益。 这些模拟将扰乱数十亿美元的社会情绪学习（SEL）市场，让学生学习和练习社交技能，就像他们在老师或治疗师那里一样，但成本只有一小部分。 目前练习和评估社交技能的解决方案依赖于面对面的互动，这是昂贵的，难以扩展。 由于复制人类语言的复杂性所需的技术挑战，产生社会丰富的开放式模拟以前是不可行的。 该项目使用一种创新技术克服了这些障碍，该技术有可能通过大规模提供实时个性化反馈来彻底改变人们学习和实践社交技能的方式。 作为该项目基础的社会行为捕获（So-Cap）技术的未来应用范围从上下文感知智能个人助理到社会感知机器人。该项目对开放式自然语言输入做出真实响应的前所未有的能力依赖于人工智能（AI）的创新众包方法，该方法赋予机器理解上下文对话并参与涵盖多个主题的扩展对话的能力。 个性化的反应是根据用户的输入，从一个巨大的数据库中提取的人类对话记录，从在线角色扮演中捕捉。 从这些数据中挖掘有意义的模式的过程也采用了众包，依靠在线雇用的非专家来聚类和注释单词，话语和事件。 由这些模式形成的计算模型为实时对话引擎提供动力，该引擎通过结合人工智能技术（包括计划识别和基于案例的规划）来选择响应。 这项研究的目标是开创一个实用的，可重复的过程，使社会模拟的生产民主化，通过提供大规模的社会技能实践，最大限度地降低成本，同时最大限度地提高社会影响。