Architecture for Building Conversational Agents that Support Collaborative Learning
Architecture for Building Conversational Agents that Support Collaborative Learning
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DOI：
10.1109/tlt.2010.41
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发表时间：
2011-01-01
影响因子：
3.7
通讯作者：
Rose, Carolyn P.
中科院分区：
文献类型：
--
作者：
Kumar, Rohit;Rose, Carolyn P.
多个研究小组已经证明，采用会话代理（CA）在一对一辅导环境中向学习者提供教学内容的会话对话系统在多个学习领域中是有效的。我们的工作重点是将这种成功的学习技术扩展到涉及两个或多个学习者与一个或多个代理交互的协作学习环境。从扩展现有的技术开发会话代理到多学习者设置的经验突出了两个基本的假设，从一个学习者设置不概括以及多用户设置，从而造成困难。这些假设包括我们所称的接近平均参与假设和已知收件人假设。一个新的软件架构Basilica允许我们解决和克服这些限制，这是本文的主要贡献。Basilica架构采用面向对象的方法将代理表示为由我们称为行为组件的网络组成，因为它们使代理能够参与丰富的会话行为。此外，我们描述了三个特定的会话代理建立使用巴西利卡，以说明这种新架构的理想属性。
Tutorial Dialog Systems that employ Conversational Agents (CAs) to deliver instructional content to learners in one-on-one tutoring settings have been shown to be effective in multiple learning domains by multiple research groups. Our work focuses on extending this successful learning technology to collaborative learning settings involving two or more learners interacting with one or more agents. Experience from extending existing techniques for developing conversational agents into multiple-learner settings highlights two underlying assumptions from the one-learner setting that do not generalize well to the multiuser setting, and thus cause difficulties. These assumptions include what we refer to as the near-even participation assumption and the known addressee assumption. A new software architecture called Basilica that allows us to address and overcome these limitations is a major contribution of this article. The Basilica architecture adopts an object-oriented approach to represent agents as a network composed of what we refer to as behavioral components because they enable the agents to engage in rich conversational behaviors. Additionally, we describe three specific conversational agents built using Basilica in order to illustrate the desirable properties of this new architecture.