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Knowledge Representation for Culturally Competent Personal Robots: Requirements, Design Principles, Implementation, and Assessment

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Abstract

Culture, intended as the set of beliefs, values, ideas, language, norms and customs which compose a person’s life, is an essential element to know by any robot for personal assistance. Culture, intended as that person’s background, can be an invaluable source of information to drive and speed up the process of discovering and adapting to the person’s habits, preferences and needs. This article discusses the requirements posed by cultural competence on the knowledge management system of a robot. We propose a framework for cultural knowledge representation that relies on (i) a three-layer ontology for storing concepts of relevance, culture-specific information and statistics, person-specific information and preferences; (ii) an algorithm for the acquisition of person-specific knowledge, which uses culture-specific knowledge to drive the search; (iii) a Bayesian Network for speeding up the adaptation to the person by propagating the effects of acquiring one specific information onto interconnected concepts. We have conducted a preliminary evaluation of the framework involving 159 Italian and German volunteers and considering 122 among habits, attitudes and social norms.

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Notes

  1. This rationale is at the core of the H2020 project CARESSES (http://caressesrobot.org/), which aims at the development of culturally competent robots for elderly care. One of the key research areas of CARESSES is denoted as Transcultural Robotic Nursing [5], which is, ideally, the bridge between culturally competent human caregivers and culturally competent robot caregivers.

  2. The current corpus of Guidelines for Culturally Competent Robot Behaviours, together with a set of scenarios grounding them in daily life situations, is freely available at: http://caressesrobot.org/en/2018/03/08/caresses-scenarios-and-guidelines-available/.

  3. For example using user-friendly tools such as Protégé: https://protege.stanford.edu/.

  4. http://www.w3.org/TR/owl-time/

  5. http://www.bbc.co.uk/ontologies.

  6. We prefer the term “instance” to the OWL-2 term “individual” because the latter is commonly used as a synonym of “person”, which might lead to confusion in this article.

  7. We introduce the term likeliness for two reasons: (i) to highlight the fact that it is not necessarily the result of statistical analyses, but it can also be provided by experts on the basis of qualitative assessment; (ii) to provide a unique name for the a posteriori probability (see Definition 1), the conditional probability (see Definition 2) and the evidence (see Definition 3), which our algorithms for the Assessment & Adaptation (see Sect. 3.4) use concurrently.

  8. https://en.wikipedia.org/wiki/Breakfast#Italy.

  9. As usual, only object and data properties that are relevant for the discussion are shown.

  10. \(T_{\mathtt {C}}\) is built from \(A_{\mathtt {C}}\) using OWL-2 APIs. In principle, building \(T_{\mathtt {C}}\) as a separate structure is not required, since the tree–like structure of \(A_{\mathtt {C}}\) can be directly explored using OWL-2 APIs. From this perspective, Algorithm 1 shall be interpreted as providing information about how \(A_{\mathtt {C}}\) is explored by Algorithms 2 and 3.

  11. In the current implementation, we use the API to create belief networks provided by Netica. See: https://www.norsys.com/netica.html.

  12. On the opposite, two nodes with a common predecessor are conditionally independent given that their predecessor is known, i.e., \(\mathsf {P(HAM\_EATING... | PENTECOST..., GEN)}= \mathsf {P(HAM\_EATING... | GEN)}\).

  13. German version: https://tinyurl.com/ybvr4xeo;

    Italian version: https://tinyurl.com/y8b3zuub.

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Acknowledgements

We are grateful to reviewer 3 whose insightful and constructive comments have greatly improved the quality of the article, guided us in our research, and inspired us in our service as reviewers.

Funding

This work has been supported by the European Commission Horizon2020 Research and Innovation Programme under Grant Agreement No. 737858 (CARESSES).

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Author notes

  1. Barbara Bruno and Carmine Tommaso Recchiuto contributed equally.

Authors and Affiliations

  1. University of Genoa, via Opera Pia 13, 16145, Genoa, Italy

    Barbara Bruno, Carmine Tommaso Recchiuto, Roberto Menicatti, Fulvio Mastrogiovanni, Renato Zaccaria & Antonio Sgorbissa

  2. Middlesex University Higher Education Corporation, The Burroughs, Hendon, London, NW4 4BT, UK

    Irena Papadopoulos & Christina Koulouglioti

  3. Örebro University, Fakultetsgatan 1, S-70182, Örebro, Sweden

    Alessandro Saffiotti

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  1. Barbara Bruno
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Correspondence to Barbara Bruno.

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Bruno, B., Recchiuto, C.T., Papadopoulos, I. et al. Knowledge Representation for Culturally Competent Personal Robots: Requirements, Design Principles, Implementation, and Assessment. Int J of Soc Robotics 11, 515–538 (2019). https://doi.org/10.1007/s12369-019-00519-w

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