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Dempster-Shafer theoretic resolution of referential ambiguity

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Abstract

Robots designed to interact with humans in realistic environments must be able to handle uncertainty with respect to the identities and properties of the people, places, and things found in their environments. When humans refer to these entities using under-specified language, robots must often generate clarification requests to determine which entities were meant. In this paper, we first present recommendations for designers of robots needing to generate such requests. We then show how a Dempster-Shafer theoretic pragmatic reasoning component capable of generating requests to clarify pragmatic uncertainty can also generate requests to resolve referential uncertainty when integrated with probabilistic reference resolution and referring expression generation components. Our system is then demonstrated in a simulated alpine search and rescue context enabled by a novel hybrid architecture.

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Notes

  1. While not directly relevant to the present work, there has also been research on using interaction patterns to identify opportunities for clarification in situated settings (Carrillo and Topp 2016).

  2. Future research will be needed to determine how the content of the options to be offered may impact how this decision is made. The results of such research may suggest refinements of this recommendation.

  3. We have chosen to adopt (and extend) the notation used in Tellex et al. (2011) in order to facilitate easier comparison to related work. We would advocate for its adoption as a common notation across the reference resolution and symbol grounding communities.

  4. Note that for most utterances j will be very small, and k will in almost all circumstances be either 1 or 2.

  5. Note, however, that low-probability hypotheses are pruned out during the resolution process, and thus the remaining hypotheses have a higher concentration of mass (and thus, higher belief and plausibility) than they would if this pruning process were not employed. This pruning process is further described by Williams et al. (2016).

  6. Some other groups have, since the publication of our original work on this topic (Williams et al. 2015), followed a similar approach, notably in the Rational Speech Act Theory inspired robotics literature (Fried et al. 2017) and in work on “inverse semantics” (Knepper et al. 2015, 2017). See also both prior and posterior work on language understanding from the Rational Speech Act psychological literature (Goodman and Stuhlmüller 2013; Goodman and Frank 2016), as well as critiques of such approaches (Gatt et al. 2013; Qing and Franke 2015).

  7. It is important to note that our pragmatic reasoning system currently is only equipped to handle conventionalized Indirect Speech Acts. For a comprehensive handling of ISAs, it will be necessary to integrate this approach with a plan reasoning system (Perrault and Allen 1980; Briggs and Scheutz 2013; Trott and Bergen 2017).

  8. Note here that we have chosen to use rules, in our example as well as in our evaluation, that use the form “Do you need Y or Z” rather than the more indirect and hence more polite “Would you like Y or Z”. These two forms trade off between our desiderata. “Do you need Y or Z” (in response to “I need X” better demonstrates intentions, but is less pragmatically appropriate, than “Would you like Y or Z”, and vice versa. Although we are able to generate both forms using the presented approach, we chose to use the form “Do you need Y or Z”, in part because, while it may be less pragmatically appropriate than “Would you like Y or Z”, both forms are significantly more pragmatically appropriate than the use of a direct command.

  9. As above, the probabilities of different properties holding for these objects were arbitrarily hand-selected for the sake of a clear and simple demonstration walkthrough. A set of “dummy” Consultants were used that provided these hand-selected probabilities when asked for probability judgments. In practice, these probability judgments can be provided by arbitrary classifiers, such as those commonly used for object recognition (e.g. Redmon et al. 2016), which may often return different levels of confidence for different observed objects.

  10. Here, \(agt_1\) is changed to the agent’s name for dialogue processing.

  11. All beliefs and plausibilities in this section are rounded.

  12. The uncertainty intervals associated with different rules were arbitrarily hand-selected for the sake of the demonstration walkthrough. For a discussion of how these intervals might be adapted over time, we direct the interested reader to (Williams et al. 2014).

  13. This Component is named after the European SHERPA project (Marconi et al. 2012) for which the alpine search and rescue KnowRob ontologies used in this integration were developed.

  14. Although, see recent discussion of the shortcomings of such checks (Hauser and Schwarz 2015), especially in crowdsourced experiments (Curran 2016).

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Acknowledgements

This work was in part funded by Grant N00014-14-1-0149 from the US Office of Naval Research. The research of Michael Beetz is partly funded by the German science foundation DFG in the context of the collaborative research centre EASE (Everyday Activity Science and Engineering).

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Authors and Affiliations

  1. MIRRORLab, Colorado School of Mines, Golden, CO, USA

    Tom Williams & Prasanth Suresh

  2. Human-Robot Interaction Laboratory, Tufts University, Medford, MA, USA

    Matthias Scheutz

  3. Institute for Artificial Intelligence, Universität Bremen, Bremen, Germany

    Fereshta Yazdani & Michael Beetz

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  1. Tom Williams
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  2. Fereshta Yazdani
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  3. Prasanth Suresh
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  4. Matthias Scheutz
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  5. Michael Beetz
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Correspondence to Tom Williams.

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This is one of several papers published in Autonomous Robots comprising the “Special Issue on Robotics Science and Systems”.

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Williams, T., Yazdani, F., Suresh, P. et al. Dempster-Shafer theoretic resolution of referential ambiguity. Auton Robot 43, 389–414 (2019). https://doi.org/10.1007/s10514-018-9795-5

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