ACM TRANSACTIONS ON INTERACTIVE INTELLIGENT SYSTEMS (TIIS) 发表有关包含某种形式的机器智能的交互式系统的设计、实现或评估的研究论文。 TIIS 文章来自广泛的研究领域和社区。一篇文章可以采用交互式智能系统的几种互补观点中的任何一种，重点关注：智能技术、用户与系统的交互，或同时关注这两个方面。

ACM Transactions on Interactive Intelligent Systems (TIIS) publishes research papers on the design, implementation, or evaluation of interactive intelligent systems that incorporate some form of machine intelligence. Articles in TIIS come from a wide range of research fields and communities. Each paper may adopt any of several complementary perspectives on interactive intelligent systems, focusing on intelligent technologies, user-system interaction, or both aspects simultaneously.

《ACM Transactions on Interactive Intelligent Systems》 (TIIS) 发表关于交互式智能系统的设计、实现或评估的研究论文，这些系统包含某种形式的机器智能。TIIS 的文章涵盖广泛的研究领域和社区。每篇文章可以从多个互补的视角探讨交互式智能系统，重点关注智能技术、用户与系统的交互，或同时关注这两个方面。

ACM Transactions on Interactive Intelligent Systems

It is essential that users understand how algorithmic decisions are made, as we increasingly delegate important decisions to intelligent systems. Prior work has often taken a techno-centric approach, focusing on new computational techniques to support transparency. In contrast, this article employs empirical methods to better understand user reactions to transparent systems to motivate user-centric designs for transparent systems. We assess user reactions to transparency feedback in four studies of an emotional analytics system. In Study 1, users anticipated that a transparent system would perform better but unexpectedly retracted this evaluation after experience with the system. Study 2 offers an explanation for this paradox by showing that the benefits of transparency are context dependent. On the one hand, transparency can help users form a model of the underlying algorithm's operation. On the other hand, positive accuracy perceptions may be undermined when transparency reveals algorithmic errors. Study 3 explored real-time reactions to transparency. Results confirmed Study 2, in showing that users are both more likely to consult transparency information and to experience greater system insights when formulating a model of system operation. Study 4 used qualitative methods to explore real-time user reactions to motivate transparency design principles. Results again suggest that users may benefit from initially simplified feedback that hides potential system errors and assists users in building working heuristics about system operation. We use these findings to motivate new progressive disclosure principles for transparency in intelligent systems and discuss theoretical implications.

Aaron Springer

Steve Whittaker

ACM Trans. Interact. Intell. Syst.

Progressive Disclosure

渐进式披露

Recent work has proposed artificial intelligence (AI) models that can learn to decide whether to make a prediction for a task instance or to delegate it to a human by considering both parties’ capabilities. In simulations with synthetically generated or context-independent human predictions, delegation can help improve the performance of human-AI teams—compared to humans or the AI model completing the task alone. However, so far, it remains unclear how humans perform and how they perceive the task when individual instances of a task are delegated to them by an AI model. In an experimental study with 196 participants, we show that task performance and task satisfaction improve for the instances delegated by the AI model, regardless of whether humans are aware of the delegation. Additionally, we identify humans’ increased levels of self-efficacy as the underlying mechanism for these improvements in performance and satisfaction, and one dimension of cognitive ability as a moderator to this effect. In particular, AI delegation can buffer potential negative effects on task performance and task satisfaction for humans with low visual processing ability. Our findings provide initial evidence that allowing AI models to take over more management responsibilities can be an effective form of human-AI collaboration in workplaces. 1

Monika Westphal

Patrick Hemmer

Michael Vössing

Max Schemmer

Sebastian Vetter

Gerhard Satzger

Towards Understanding AI Delegation: The Role of Self-Efficacy and Visual Processing Ability

走向理解人工智能委托：自我效能与视觉处理能力的作用

Building higher-quality image classification models requires better performance analysis (PA) to help understand their behaviors. We propose ConfusionLens, a dynamic and interactive visualization interface that augments a conventional confusion matrix with focus+context visualization. This interface allows users to seamlessly switch table layouts among three views (overall view, class-level view, and between-class view) while observing all of the instance images in a single screen. We designed and implemented a ConfusionLens prototype that supports hundreds of instances, and then conducted a user study (N=14) to evaluate it compared to the conventional confusion matrix with a split view of instances. Results show that ConfusionLens achieved faster task-completion time in observing instance-level performance and higher accuracy in observing between-class confusion. Moreover, we conducted an expert interview (N=6) to investigate the applicability of our interface to practical PA tasks, and then implemented several extensions of ConfusionLens based on the feedback. Feedback on these extensions from users experienced in image classification (N=5) demonstrated their general usefulness and highlighted their beneficial use in PA tasks.

Kazuyuki Fujita

Keito Uwaseki

Hongyu Bu

Kazuki Takashima

Yoshifumi Kitamura

ConfusionLens: Focus+Context Visualization Interface for Performance Analysis of Multiclass Image Classifiers

ConfusionLens：多类图像分类器性能分析的焦点+上下文可视化界面

Computer-assisted cognitive training can help patients affected by several illnesses alleviate their cognitive deficits or healthy people improve their mental performance. In most computer-based systems, training sessions consist of graded exercises, which should ideally be able to gradually improve the trainee’s cognitive functions. Indeed, adapting the difficulty of the exercises to how individuals perform in their execution is crucial to improve the effectiveness of cognitive training activities. In this article, we propose the use of reinforcement learning (RL) to learn how to automatically adapt the difficulty of computerized exercises for cognitive training. In our approach, trainees’ performance in performed exercises is used as a reward to learn a policy that changes over time the values of the parameters that determine exercise difficulty. We illustrate a method to be initially used to learn difficulty-variation policies tailored for specific categories of trainees, and then to refine these policies for single individuals. We present the results of two user studies that provide evidence for the effectiveness of our method: a first study, in which a student category policy obtained via RL was found to have better effects on the cognitive function than a standard baseline training that adopts a mechanism to vary the difficulty proposed by neuropsychologists, and a second study, demonstrating that adding an RL-based individual customization further improves the training process.

Floriano Zini

Fabio Le Piane

Mauro Gaspari

Adaptive Cognitive Training with Reinforcement Learning

自适应认知训练与强化学习

A key objective in the design of graphical user interfaces (GUIs) is to ensure consistency across screens of the same product. However, designing a compliant layout is time-consuming and can distract designers from creative thinking. This paper studies layout recommendation methods that fulfill such consistency requirements using machine learning. Given a desired element type and size, the methods suggest element placements following real-world GUI design processes. Consistency requirements are given implicitly through previous layouts from which patterns are to be learned, comparable to existing screens of a software product. We adopt two recently proposed methods for this task, a Graph Neural Network (GNN) and a Transformer model, and compare them with a custom approach based on sequence alignment and nearest neighbor search (kNN) . The methods were tested on handcrafted datasets with explicit layout patterns, as well as large-scale public datasets of diverse mobile design layouts. Our results show that our instance-based learning algorithm outperforms both neural network approaches. Ultimately, this work contributes to establishing smarter design tools for professional designers with explainable algorithms that increase their efficacy.

Lukas Brückner

Luis A. Leiva

Antti Oulasvirta

Learning GUI Completions with User-defined Constraints

学习带有用户定义约束的GUI完成

Autonomous negotiating agents, which can interact with other agents, aim to solve decision-making problems involving participants with conflicting interests. Designing agents capable of negotiating with human partners requires considering some factors, such as emotional states and arguments. For this purpose, we introduce an extended taxonomy of argument types capturing human speech acts during the negotiation. We propose an argument-based automated negotiating agent that can extract human arguments from a chat-based environment using a hierarchical classifier. Consequently, the proposed agent can understand the received arguments and adapt its strategy accordingly while negotiating with its human counterparts. We initially conducted human-agent negotiation experiments to construct a negotiation corpus to train our classifier. According to the experimental results, it is seen that the proposed hierarchical classifier successfully extracted the arguments from the given text. Moreover, we conducted a second experiment where we tested the performance of the designed negotiation strategy considering the human opponent’s arguments and emotions. Our results showed that the proposed agent beats the human negotiator and gains higher utility than the baseline agent.

Anıl Doğru

Mehmet Onur Keskin

Reyhan Aydoğan

Taking into Account Opponent’s Arguments in Human-Agent Negotiations

考虑对手论点的人机谈判

This paper introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of depth sensors and cameras. These limitations confine object detection to close proximity and hinder perception beyond immediate surroundings, through obstructions, or across different structural levels, impacting collaborative use. It harnesses the Inertial Measurement Unit (IMU) in AR devices to measure users’ relative distances from a set physical point, enabling object detection sharing among multiple users across obstacles like walls and over distances. We tested Ajna's effectiveness in a controlled study with 15 participants simulating emergency situations in a multi-story building. We found that Ajna improved object detection, location awareness, and situational awareness, and reduced search times by 15%. Ajna's performance in simulated environments highlights the potential of artificial intelligence (AI) to enhance sensemaking in critical situations, offering insights for law enforcement, search and rescue, and infrastructure management.

Matthew Wilchek

Kurt Luther

Feras A. Batarseh

Ajna: A Wearable Shared Perception System for Extreme Sensemaking

Ajna：一种用于极端情境感知的外穿戴共享感知系统

Sketch-based drawing assessments in art therapy are widely used to understand individuals’ cognitive and psychological states, such as cognitive impairments or mental disorders. Along with self-reported measures based on questionnaires, psychological drawing assessments can augment information regarding an individual’s psychological state. Interpreting drawing assessments demands significant time and effort, particularly for large groups such as schools or companies, and relies on the expertise of art therapists. To address this issue, we propose an artificial intelligence (AI)-based expert support system called AlphaDAPR to support art therapists and psychologists in conducting large-scale automatic drawing assessments. In Study 1, we first investigated user experience in AlphaDAPR . Through surveys involving 64 art therapists, we observed a substantial willingness (64.06% of participants) in using the proposed system. Structural equation modeling highlighted the pivotal role of explainable AI in the interface design, affecting perceived usefulness, trust, satisfaction, and intention to use. However, our interviews unveiled a nuanced perspective: while many art therapists showed a strong inclination to use the proposed system, they also voiced concerns about potential AI limitations and risks. Since most concerns arose from insufficient trust, which was the focal point of our attention, we conducted Study 2 with the aim of enhancing trust. Study 2 delved deeper into the necessity of clear communication regarding the division of roles between AI and users for elevating trust. Through experimentation with another 26 art therapists, we demonstrated that clear communication enhances users’ trust in our system. Our work not only highlights the potential of AlphaDAPR to streamline drawing assessments but also underscores broader implications for human-AI collaboration in psychological domains. By addressing concerns and optimizing communication, we pave the way for a symbiotic relationship between AI and human expertise, ultimately enhancing the efficacy and accessibility of psychological assessment tools.

短名	ACM Trans. Interact. Intell. Syst.
Journal Impact	3.64
国际分区	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE(Q2)
期刊索引	SCI Q3中科院 4 区

ISSN	2160-6455, 2160-6463
h-index	45
国内分区	计算机科学(4区)计算机科学计算机人工智能(4区)

涉及主题	计算机科学人工智能人机交互哲学生物工程类心理学操作系统数学程序设计语言语言学物理万维网机器学习经济地理政治学法学认识论社会学计算机视觉量子力学历史数据科学数据挖掘考古统计
出版信息	出版商: Association for Computing Machinery (ACM)，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: 2011，原创研究文献占比: 100.00%，自引率:2.80%， Gold OA占比: 4.40%

ACM Transactions on Interactive Intelligent Systems

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Progressive Disclosure

Towards Understanding AI Delegation: The Role of Self-Efficacy and Visual Processing Ability

ConfusionLens: Focus+Context Visualization Interface for Performance Analysis of Multiclass Image Classifiers

Adaptive Cognitive Training with Reinforcement Learning

Learning GUI Completions with User-defined Constraints

ConfusionLens: Focus+Context Visualization Interface for Performance Analysis of Multiclass Image Classifiers

Towards Understanding AI Delegation: The Role of Self-Efficacy and Visual Processing Ability

Taking into Account Opponent’s Arguments in Human-Agent Negotiations

Ajna: A Wearable Shared Perception System for Extreme Sensemaking

Developing an AI-based Explainable Expert Support System for Art Therapy