Facilitating user involvement in intelligent interactive systems

智能交互系统嵌入人工智能（AI）领域的技术，以定制其人机交互以满足个人用户的需求。给定正确的底层推理机制，智能交互系统在与复杂应用程序和信息空间进行定性（例如，用户满意度）和定量（例如，任务效率）交互时，具有巨大的潜力来改善用户体验。然而，它们的成功不仅依赖于对相关用户和上下文因素进行建模和推理的能力，还依赖于传递智能的方式，或者换句话说，依赖于交互设计。交互设计中特别重要的是为用户参与提供足够的机会。拟议的研究将探讨诸如如何使系统的智能行为可以理解，如何在用户和系统之间分工，以便发挥各自的优势，以及在什么情况下智能交互系统应该提供主动建议。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使工业界和研究界受益。在加拿大和全世界有数百万用户与技术进行互动，从个人用户的角度来看，如果提供适当的智能支持，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），这些技术可以大大提高效率和效果。在研究界，这项工作将帮助那些设计潜在的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，糟糕的可用性不会成为一个混淆因素。这项工作的第二个贡献将是建立和研究智能交互系统的强有力的方法论基础。

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.