Since its inception in 2016, the Alexa Prize program has enabled hundreds of university students to explore and compete to develop conversational agents through the SocialBot Grand Challenge. The goal of the challenge is to build agents capable of conversing coherently and engagingly with humans on popular topics for 20 minutes, while achieving an average rating of at least 4.0/5.0. However, as conversational agents attempt to assist users with increasingly complex tasks, new conversational AI techniques and evaluation platforms are needed. The Alexa Prize TaskBot challenge, established in 2021, builds on the success of the SocialBot challenge by introducing the requirements of interactively assisting humans with real-world Cooking and Do-It-Yourself tasks, while making use of both voice and visual modalities. This challenge requires the TaskBots to identify and understand the user's need, identify and integrate task and domain knowledge into the interaction, and develop new ways of engaging the user without distracting them from the task at hand, among other challenges. This paper provides an overview of the TaskBot challenge, describes the infrastructure support provided to the teams with the CoBot Toolkit, and summarizes the approaches the participating teams took to overcome the research challenges. Finally, it analyzes the performance of the competing TaskBots during the first year of the competition.
翻译:自2016年启动以来,亚历山大奖方案使数百名大学生得以探索和竞争,通过 " 社会博特大挑战 " 开发对话代理器。挑战的目标是培养能够就流行主题与人进行一致和互动的代理器,时间为20分钟,同时达到平均评分至少4.0/5.0。然而,由于对话代理器试图协助用户完成日益复杂的任务,因此需要新的对话AI技术和评价平台。2021年设立的亚历山大奖任务博特挑战以社会博特挑战的成功为基础,通过引入互动协助人类执行真实世界烹饪和 Do-It-self任务的要求,同时利用声音和视觉模式。这一挑战要求任务博特小组确定和理解用户的需求,确定和将任务和领域知识纳入互动,并开发新的接触用户而又不分散他们手头任务的方法,以及其他挑战。本文概述了任务小组面临的第一个挑战,介绍了向团队提供基础设施支持,以实际世界烹饪和“做自己做自己做自己”的要求,同时利用声音和视觉模式。这一挑战要求任务小组确定和理解用户的需求,确定和理解用户的需求,将任务和领域知识纳入互动分析的最后参与小组的工作,从而克服工作的挑战。