Facilitating user involvement in intelligent interactive systems

智能交互系统嵌入了人工智能（AI）领域的技术，以根据个人用户需求定制人机交互。 给定正确的底层推理机制，智能交互系统具有很大的潜力来改善用户的体验，因为它们与复杂的应用程序和信息空间进行定性交互（例如，用户满意度）和定量地（例如，任务效率）。然而，他们的成功不仅取决于他们对相关用户和情境因素进行建模和推理的能力，还取决于智能传递的方式，或者换句话说，交互设计。 在交互设计中，特别重要的是为用户参与提供足够的机会。 拟议的研究将探讨的问题，如如何使系统的智能行为的理解，如何分工之间的用户和系统，使他们各自的优势发挥杠杆作用，以及在什么情况下，智能互动系统应提供积极主动的意见。 这些问题将通过两种互补的方法进行研究：i）使用定性方法，如半结构化访谈，以了解用户如何感知和使用部署的智能交互系统; ii）创建和系统地评估研究原型，以评估其设计方面的影响。 更好地理解如何设计智能交互系统将有利于工业界和研究界。 在加拿大和世界各地有数百万用户与技术进行交互，从个人用户的角度来看，如果提供正确的智能支持，包括搜索引擎，电子商务网站和生产力应用程序（例如，文字处理器）。 在研究界，这项工作将帮助那些设计底层人工智能推理机制的人，以确保在涉及人类参与者的任何验证工作中，可用性差不会成为一个混杂因素。这项工作的第二个贡献将是一个强大的方法论基础，建立和研究智能交互系统。

Intelligent interactive systems embed techniques from the field of artificial intelligence (AI) to tailor their human-computer interactions to individual user needs. Given the right underlying reasoning mechanisms, intelligent interactive systems have great potential to improve users' experience as they interact with complex applications and information spaces both qualitatively (e.g., user satisfaction) and quantitatively (e.g., task efficiency). Their success, however, relies not only on their ability to model and reason about relevant user and contextual factors, but also on the manner in which the intelligence is delivered, or in other words, the interaction design. Of particular importance in the interaction design is providing sufficient opportunities for user involvement. The proposed research will examine issues such as how to make the system's intelligent behaviour understandable, how to divide the labour between the user and system so that their respective strengths are leveraged, and under what circumstances an intelligent interactive system should provide proactive advice. These issues will be examined through two complementary approaches: i) using qualitative methods, such as semi-structured interviews, to understand how users perceive and use deployed intelligent interaction systems; and ii) creating and systematically evaluating research prototypes to assess the impact of aspects of their design. A better understanding of how to design intelligent interactive systems will benefit both industrial and research communities. There are millions users in Canada and worldwide interacting with technology that could be made substantially more efficient and effective from an individual user's perspective given the right intelligent support, including search engines, e-commerce websites and productivity applications (e.g., word processors). Within the research community, this work will help those designing underlying AI reasoning mechanisms to ensure that poor usability is not a confounding factor in any validation work involving human participants. A second contribution of this work will be a strong methodological basis with which to build and study intelligent interactive systems.