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Demonstration + Natural Language: Multimodal Interfaces for GUI-Based Interactive Task Learning Agents

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Artificial Intelligence for Human Computer Interaction: A Modern Approach

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

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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Notes

  1. 1.

    Sugilite is named after a purple gemstone, and stands for: Smartphone Users Generating Intelligent Likeable Interfaces Through Examples.

  2. 2.

    A demo video is available at https://www.youtube.com/watch?v=tdHEk-GeaqE.

  3. 3.

    https://github.com/tobyli/Sugilite_development.

  4. 4.

    Sovite is named after a type of rock. It is also an acronym for System for Optimizing Voice Interfaces to Tackle Errors.

  5. 5.

    Available at: https://github.com/tobyli/screen2vec.

  6. 6.

    Available at: http://interactionmining.org/rico.

  7. 7.

    Since the next screen is always within the same app, and therefore, shares an app description embedding, the prediction task favors having information about the specific app (i.e., app store description embedding) dominate the embedding

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Acknowledgements

This research was supported in part by Verizon through the Yahoo! InMind project, a J.P. Morgan Faculty Research Award, NSF grant IIS-1814472, AFOSR grant FA95501710218, and Google Cloud Research Credits. Any opinions, findings or recommendations expressed here are those of the authors and do not necessarily reflect views of the sponsors. We thank Amos Azaria, Yuanchun Li, Fanglin Chen, Igor Labutov, Xiaohan Nancy Li, Xiaoyi Zhang, Wenze Shi, Wanling Ding, Marissa Radensky, Justin Jia, Kirielle Singarajah, Jingya Chen, Brandon Canfield, Haijun Xia, and Lindsay Popowski for their contributions to this project.

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    Toby Jia-Jun Li, Tom M. Mitchell & Brad A. Myers

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    Otmar Hilliges

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Li, T.JJ., Mitchell, T.M., Myers, B.A. (2021). Demonstration + Natural Language: Multimodal Interfaces for GUI-Based Interactive Task Learning Agents. In: Li, Y., Hilliges, O. (eds) Artificial Intelligence for Human Computer Interaction: A Modern Approach. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-030-82681-9_15

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