Teaching Robots to Act and Converse in Physical Spaces: Participatory Design Fictions with Museum Guides

Candello, Heloísa; Pichiliani, Mauro Carlos; Wessel, Mairieli; Pinhanez, Cláudio; Müller, Michael

doi:10.1145/3363384.3363399

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Tsekleves et al 2017 and 2019 [45] [46], Nägele et al 2018 [35], Candello et al 2019 [7] and Desjardins et al 2019 [11], among others, have also done studies of PDFi which gave some degree of editorial control to the participants.…”

Section: B What Is Novel About This Work?mentioning

confidence: 99%

“…Candello et al [7] provided the beginning of a story -about robot floor guides in museums -and asked participants to complete it.…”

Section: B What Is Novel About This Work?mentioning

confidence: 99%

“…For example, Reviewer 2 asserts that DF is NOT storytelling. That assertion is trivially refutable through the literature, because the question of whether DF is storytelling has been asked, and the answer from the World Design Fiction Council 7 was that it can be [9]. Of course this too is a matter of opinion, but it is an opinion that is expressed in The Literature.…”

Section: A I'd Like To Have An Argument Pleasementioning

confidence: 99%

See 2 more Smart Citations

Eff the ineffable: on the uncommunicability of a conceptually simple contribution to HCI methodology

Oliver¹,

Mortier²,

Crowcroft³

2022

Preprint

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Section: B What Is Novel About This Work?mentioning

confidence: 99%

“…Candello et al [7] provided the beginning of a story -about robot floor guides in museums -and asked participants to complete it.…”

Section: B What Is Novel About This Work?mentioning

confidence: 99%

Section: A I'd Like To Have An Argument Pleasementioning

confidence: 99%

See 1 more Smart Citation

Eff the ineffable: on the uncommunicability of a conceptually simple contribution to HCI methodology

Oliver¹,

Mortier²,

Crowcroft³

2022

Preprint

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show abstract

“…Some of the publications did not specify how data was presented in their problem setting. Most of these papers have more focus on the human-computer interaction or human experience [49]- [53] and did not detail the process of how data was presented. Telle et al had a more theoretical focus and introduced the novel concept teaching size as a measurement to be used in analysis of machine teaching [54].…”

Section: Data Presentation To the Learnermentioning

confidence: 99%

Interactive Online Machine Learning

Tegen¹

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With the Internet of Things paradigm, the data generated by the rapidly increasing number of connected devices lead to new possibilities, such as using machine learning for activity recognition in smart environments. However, it also introduces several challenges. The sensors of different devices might be mobile and of different types, i.e. there is a need to handle streaming data from a dynamic and heterogeneous set of sensors. In machine learning, the performance is often linked to the availability and quality of annotated data. Annotating data is in general costly, but it can be even more challenging if there is not any, or a very small amount of, annotated data to train the model on at the start of learning. To handle these issues, we implement interactive and adaptive systems. By including human-in-the-loop, which we refer to as interactive machine learning, the input from users can be utilized to build the model. The type of input used in interactive machine learning is typically annotations of the data, i.e. correctly labelled data points. Generally, it is assumed that the user always provides correct labels in accordance with the chosen interactive learning strategy. In many real-world applications these assumptions are not realistic however, as users might provide incorrect labels or not provide labels at all in line with the chosen strategy. In this thesis we explore which interactive learning strategy types are possible in the given scenario and how they affect performance, as well as the effect of machine learning algorithms on the performance. We also study how a user who is not always reliable, i.e. who does not always provide a correct label when expected to, can affect performance. We propose a taxonomy of interactive online machine learning strategies and test how the different strategies affect performance through experiments on multiple datasets. Simulated experiments are compared to experiments with human participants, to verify the results. The findings show that the overall best performing interactive learning strategy is one where the user provides labels when current estimations are incorrect, but that the best performing machine learning algorithm depends on the problem scenario. The experiments also show that a decreased reliability of the user leads to decreased performance, especially when there is a limited amount of labelled data. The robustness of the machine learning algorithms differs, where e.g. Naïve Bayes classifier is better at handling a lower reliability of the user. We also present a systematic literature review on machine teaching, a subfield of interactive machine learning where the human is proactive in the interaction. The study shows that the area of machine teaching is rapidly evolving with an increased number of publications in recent years. However, as it is still maturing, there exists several open challenges that would benefit from further exploration, e.g. how human factors can affect performance.

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A systematic review of artificial intelligence technologies used for story writing

Fang

Leung

et al. 2023

Educ Inf Technol

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Teaching Robots to Act and Converse in Physical Spaces: Participatory Design Fictions with Museum Guides

Cited by 8 publications

References 22 publications

Eff the ineffable: on the uncommunicability of a conceptually simple contribution to HCI methodology

Eff the ineffable: on the uncommunicability of a conceptually simple contribution to HCI methodology

Interactive Online Machine Learning

A systematic review of artificial intelligence technologies used for story writing

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