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1. Interactively Explaining Robot Policies to Humans in Integrated Virtual and Physical Training Environments

   https://doi.org/10.1145/3610978.3640656  

   Qian, Peizhu; Unhelkar, Vaibhav (March 2024, Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction)

   Policy summarization is a computational paradigm for explaining the behavior and decision-making processes of autonomous robots to humans. It summarizes robot policies via exemplary demonstrations, aiming to improve human understanding of robotic behaviors. This understanding is crucial, especially since users often make critical decisions about robot deployment in the real world. Previous research in policy summarization has predominantly focused on simulated robots and environments, overlooking its application to physically embodied robots. Our work fills this gap by combining current policy summarization methods with a novel, interactive user interface that involves physical interaction with robots. We conduct human-subject experiments to assess our explanation system, focusing on the impact of different explanation modalities in policy summarization. Our findings underscore the unique advantages of combining virtual and physical training environments to effectively communicate robot behavior to human users. 

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2. Co-Designing with Users the Explanations for a Proactive Auto-Response Messaging Agent

   https://doi.org/10.1145/3604248  

   Jain, Pranut; Farzan, Rosta; Lee, Adam J. (September 2023, Proceedings of the ACM on Human-Computer Interaction)

   Explanations of AI Agents' actions are considered to be an important factor in improving users' trust in the decisions made by autonomous AI systems. However, as these autonomous systems evolve from reactive, i.e., acting on user input, to proactive, i.e., acting without requiring user intervention, there is a need to explore how the explanation for the actions of these agents should evolve. In this work, we explore the design of explanations through participatory design methods for a proactive auto-response messaging agent that can reduce perceived obligations and social pressure to respond quickly to incoming messages by providing unavailability-related context. We recruited 14 participants who worked in pairs during collaborative design sessions where they reasoned about the agent's design and actions. We qualitatively analyzed the data collected through these sessions and found that participants' reasoning about agent actions led them to speculate heavily on its design. These speculations significantly influenced participants' desire for explanations and the controls they sought to inform the agents' behavior. Our findings indicate a need to transform users' speculations into accurate mental models of agent design. Further, since the agent acts as a mediator in human-human communication, it is also necessary to account for social norms in its explanation design. Finally, user expertise in understanding their habits and behaviors allows the agent to learn from the user their preferences when justifying its actions. 

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3. Interactive Task Learning from GUI-Grounded Natural Language Instructions and Demonstrations

   https://doi.org/10.18653/v1/2020.acl-demos.25  

   Li, Toby Jia-Jun; Mitchell, Tom; Myers, Brad (July 2020, The AAAI-20 Workshop on Intelligent Process Automation (IPA-20))

   null (Ed.)

   We summarize our past five years of work on designing, building, and studying Sugilite, an interactive task learning agent that can learn new tasks and relevant associated concepts interactively from the user’s natural language instructions and demonstrations leveraging the graphical user interfaces (GUIs) of third-party mobile apps. Through its multi-modal and mixed-initiative approaches for Human- AI interaction, Sugilite made important contributions in improving the usability, applicability, generalizability, flexibility, robustness, and shareability of interactive task learning agents. Sugilite also represents a new human-AI interaction paradigm for interactive task learning, where it uses existing app GUIs as a medium for users to communicate their intents with an AI agent instead of the interfaces for users to interact with the underlying computing services. In this chapter, we describe the Sugilite system, explain the design and implementation of its key features, and show a prototype in the form of a conversational assistant on Android. 

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4. Demonstration + Natural Language: Multimodal Interfaces for GUI-based Interactive Task Learning Agents

   https://doi.org/10.1007/978-3-030-82681-9_15  

   Li, Toby Jia-Jun; Mitchell, Tom M.; Myers, Brad A (May 2021, Artificial Intelligence for Human Computer Interaction: A Modern Approach)

   Li, Yang; Hilliges, Otmar (Ed.)

   We summarize our past five years of work on designing, building, and studying Sugilite, an interactive task learning agent that can learn new tasks and relevant associated concepts interactively from the user’s natural language instructions and demonstrations leveraging the graphical user interfaces (GUIs) of third-party mobile apps. Through its multi-modal and mixed-initiative approaches for Human-AI interaction, Sugilite made important contributions in improving the usability, applicability, generalizability, flexibility, robustness, and shareability of interactive task learning agents. Sugilite also represents a new human-AI interaction paradigm for interactive task learning, where it uses existing app GUIs as a medium for users to communicate their intents with an AI agent instead of the interfaces for users to interact with the underlying computing services. In this chapter, we describe the Sugilite system, explain the design and implementation of its key features, and show a prototype in the form of a conversational assistant on Android. 

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5. Socially situated artificial intelligence enables learning from human interaction

   https://doi.org/10.1073/pnas.2115730119  

   Krishna, Ranjay; Lee, Donsuk; Fei-Fei, Li; Bernstein, Michael S. (September 2022, Proceedings of the National Academy of Sciences)

   Regardless of how much data artificial intelligence agents have available, agents will inevitably encounter previously unseen situations in real-world deployments. Reacting to novel situations by acquiring new information from other people—socially situated learning—is a core faculty of human development. Unfortunately, socially situated learning remains an open challenge for artificial intelligence agents because they must learn how to interact with people to seek out the information that they lack. In this article, we formalize the task of socially situated artificial intelligence—agents that seek out new information through social interactions with people—as a reinforcement learning problem where the agent learns to identify meaningful and informative questions via rewards observed through social interaction. We manifest our framework as an interactive agent that learns how to ask natural language questions about photos as it broadens its visual intelligence on a large photo-sharing social network. Unlike active-learning methods, which implicitly assume that humans are oracles willing to answer any question, our agent adapts its behavior based on observed norms of which questions people are or are not interested to answer. Through an 8-mo deployment where our agent interacted with 236,000 social media users, our agent improved its performance at recognizing new visual information by 112%. A controlled field experiment confirmed that our agent outperformed an active-learning baseline by 25.6%. This work advances opportunities for continuously improving artificial intelligence (AI) agents that better respect norms in open social environments. 

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