Towards the design of user-centric strategy recommendation systems for collaborative Human–AI tasks

IF 5.3 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS
Lakshita Dodeja , Pradyumna Tambwekar , Erin Hedlund-Botti, Matthew Gombolay
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引用次数: 0

Abstract

Artificial Intelligence is being employed by humans to collaboratively solve complicated tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by understanding user preferences and recommending different strategies for solving the particular task to humans. Prior work has focused on personalization of recommendation systems for relatively well-understood tasks in the context of e-commerce or social networks. In this paper, we seek to understand the important factors to consider while designing user-centric strategy recommendation systems for decision-making. We conducted a human-subjects experiment (n=60) for measuring the preferences of users with different personality types towards different strategy recommendation systems. We conducted our experiment across four types of strategy recommendation modalities that have been established in prior work: (1) Single strategy recommendation, (2) Multiple similar recommendations, (3) Multiple diverse recommendations, (4) All possible strategies recommendations. While these strategy recommendation schemes have been explored independently in prior work, our study is novel in that we employ all of them simultaneously and in the context of strategy recommendations, to provide us an in-depth overview of the perception of different strategy recommendation systems. We found that certain personality traits, such as conscientiousness, notably impact the preference towards a particular type of system (p < 0.01). Finally, we report an interesting relationship between usability, alignment, and perceived intelligence wherein greater perceived alignment of recommendations with one’s own preferences leads to higher perceived intelligence (p < 0.01) and higher usability (p < 0.01).

Abstract Image

Abstract Image

为人机协作任务设计以用户为中心的策略推荐系统
人工智能正被人类用于协同解决搜救、制造等复杂任务。通过了解用户偏好并向人类推荐解决特定任务的不同策略,可以实现高效的团队协作。之前的工作主要集中在电子商务或社交网络背景下针对相对容易理解的任务的个性化推荐系统。在本文中,我们试图了解在设计以用户为中心的决策策略推荐系统时需要考虑的重要因素。我们进行了一项人体实验(n=60),以测量不同性格类型的用户对不同策略推荐系统的偏好。我们的实验跨越了之前工作中已经确立的四种策略推荐模式:(1) 单一策略推荐,(2) 多种相似推荐,(3) 多种不同推荐,(4) 所有可能的策略推荐。虽然这些策略推荐方案在之前的研究中都有过独立的探讨,但我们的研究却很新颖,因为我们在策略推荐的背景下同时采用了所有这些方案,从而为我们提供了对不同策略推荐系统感知的深入概览。我们发现,某些人格特质(如自觉性)会明显影响对特定类型系统的偏好(p < 0.01)。最后,我们报告了可用性、一致性和感知智力之间的有趣关系,其中,推荐与个人偏好的一致性越高,感知智力越高(p < 0.01),可用性越高(p < 0.01)。
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来源期刊
International Journal of Human-Computer Studies
International Journal of Human-Computer Studies 工程技术-计算机:控制论
CiteScore
11.50
自引率
5.60%
发文量
108
审稿时长
3 months
期刊介绍: The International Journal of Human-Computer Studies publishes original research over the whole spectrum of work relevant to the theory and practice of innovative interactive systems. The journal is inherently interdisciplinary, covering research in computing, artificial intelligence, psychology, linguistics, communication, design, engineering, and social organization, which is relevant to the design, analysis, evaluation and application of innovative interactive systems. Papers at the boundaries of these disciplines are especially welcome, as it is our view that interdisciplinary approaches are needed for producing theoretical insights in this complex area and for effective deployment of innovative technologies in concrete user communities. Research areas relevant to the journal include, but are not limited to: • Innovative interaction techniques • Multimodal interaction • Speech interaction • Graphic interaction • Natural language interaction • Interaction in mobile and embedded systems • Interface design and evaluation methodologies • Design and evaluation of innovative interactive systems • User interface prototyping and management systems • Ubiquitous computing • Wearable computers • Pervasive computing • Affective computing • Empirical studies of user behaviour • Empirical studies of programming and software engineering • Computer supported cooperative work • Computer mediated communication • Virtual reality • Mixed and augmented Reality • Intelligent user interfaces • Presence ...
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