Advertisement

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

  • Barbara BrunoEmail author
  • Carmine Tommaso Recchiuto
  • Irena Papadopoulos
  • Alessandro Saffiotti
  • Christina Koulouglioti
  • Roberto Menicatti
  • Fulvio Mastrogiovanni
  • Renato Zaccaria
  • Antonio Sgorbissa
Article

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.

Keywords

Culture-aware robotics Companion robot Knowledge representation 

Notes

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 Information

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

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Agarwal P, Verma R, Mallik A (2016) Ontology based disease diagnosis system with probabilistic inference. In: 1st India international conference on information processing (IICIP), IEEE, pp 1–5Google Scholar
  2. 2.
    Andrist S, Ziadee M, Boukaram H, Mutlu B, Sakr M (2015) Effects of culture on the credibility of robot speech: A comparison between english and arabic. In: HRI, pp 157–164Google Scholar
  3. 3.
    Baader F, Calvanese D, McGuinness D, Nardi D, Patel-Schneider P (2003) The description logic handbook: theory, implementations and applications. Cambridge University Press, New YorkzbMATHGoogle Scholar
  4. 4.
    Bateman CM (2007) Game writing: Narrative skills for videogames. Charles River Media IndependenceGoogle Scholar
  5. 5.
    Bruno B, Chong NY, Kamide H, Kanoria S, Lee J, Lim Y, Pandey AK, Papadopoulos C, Papadopoulos I, Pecora F, Saffiotti A, Sgorbissa A (2017a) Paving the way for culturally competent robots: a position paper. In: RO-MAN 2017, pp 430–435Google Scholar
  6. 6.
    Bruno B, Mastrogiovanni F, Pecora F, Sgorbissa A, Saffiotti A (2017b) A framework for culture-aware robots based on fuzzy logic. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), IEEE, pp 1–6Google Scholar
  7. 7.
    Carvalho RN, Laskey KB, Costa PC (2017) Pr-owl-a language for defining probabilistic ontologies. Int J Approx Reason 91:56–79MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Conti D, Di Nuovo S, Buono S, Di Nuovo A (2017) Robots in education and care of children with developmental disabilities: a study on acceptance by experienced and future professionals. Int J Soc Robot 9(1):51–62CrossRefGoogle Scholar
  9. 9.
    Dautenhahn K, Woods S, Kaouri C, Walters ML, Koay KL, Werry I (2005) What is a robot companion-friend, assistant or butler? In: IEEE/RSJ international conference on intelligent robots and systems (IROS 2005) IEEE, pp 1192–1197Google Scholar
  10. 10.
    Ding Z, Peng Y (2004) A probabilistic extension to ontology language owl. In: Proceedings of the 37th annual Hawaii international conference on system sciences, IEEE, p 10Google Scholar
  11. 11.
    Eresha G, Häring M, Endrass B, André E, Obaid M (2013) Investigating the influence of culture on proxemic behaviors for humanoid robots. In: RO-MAN 2013, pp 430–435Google Scholar
  12. 12.
    Evers V, Maldonado H, Brodecki T, Hinds P (2008) Relational vs. group self-construal: untangling the role of national culture in HRI. In: HRI 2008, pp 255–262Google Scholar
  13. 13.
    Flandorfer P (2012) Population ageing and socially assistive robots for elderly persons: the importance of sociodemographic factors for user acceptance. Int J Popul Res 2012:13.  https://doi.org/10.1155/2012/829835 Google Scholar
  14. 14.
    Guarino N, et al (1998) Formal ontology and information systems. In: Proceedings of FOIS, pp 81–97Google Scholar
  15. 15.
    Hall ET (1976) Beyond culture. Anchor, NorwellGoogle Scholar
  16. 16.
    Hofstede G, Hofstede GJ, Minkov M (1991) Cultures and organizations: software of the mind, vol 2. McGraw-Hill, New York cityGoogle Scholar
  17. 17.
    IEEE Standards Association (2018) 1872.1-robot task representation. http://standards.ieee.org/develop/project/1872.1.html. Accessed 18 Jan 2019
  18. 18.
    Joosse MP, Poppe RW, Lohse M, Evers V (2014) Cultural differences in how an engagement-seeking robot should approach a group of people. In: CABS 2014, pp 121–130Google Scholar
  19. 19.
    Köckemann U (2016) Constraint-based methods for human-aware planning. Ph.D. thesis, Örebro universityGoogle Scholar
  20. 20.
    Lugrin B, Frommel J, André E (2015) Modeling and evaluating a bayesian network of culture-dependent behaviors. In: Culture computing 2015, pp 33–40Google Scholar
  21. 21.
    Marios Vasiliou R, Christiana Kouta R, Vasilios Raftopoulos R (2013) The use of the cultural competence assessment tool (ccatool) in community nurses: the pilot study and test-retest reliability. Int J Caring Sci 6(1):44Google Scholar
  22. 22.
    Menicatti R, Bruno B, Sgorbissa A (2017) Modelling the influence of cultural information on vision-based human home activity recognition. In: 2017 14th international conference on ubiquitous robots and ambient intelligence (URAI), pp 32–38Google Scholar
  23. 23.
    Niles I, Pease A (2001) Towards a standard upper ontology. In: Proceedings of the international conference on formal ontology in information systems, ACM, pp 2–9Google Scholar
  24. 24.
    Nomura T, Suzuki T, Kanda T, Han J, Shin N, Burke J, Kato K (2008) What people assume about humanoid and animal-type robots: cross-cultural analysis between japan, korea, and the united states. Int J Humanoid Robot 5(01):25–46CrossRefGoogle Scholar
  25. 25.
    Papadopoulos I (2006a) The papadopoulos, tilki and taylor model of developing cultural competence. Transcultural health and social care: development of culturally competent practitioners, pp 7–24Google Scholar
  26. 26.
    Papadopoulos I (2006b) Transcultural health and social care: development of culturally competent practitioners. Elsevier, AmsterdamGoogle Scholar
  27. 27.
    Patompak P, Jeong S, Nilkhamhang I, Chong NY (2017) Learning social relations for culture aware interaction. In: 2017 14th international conference on ubiquitous robots and ambient intelligence (URAI), pp 26–31Google Scholar
  28. 28.
    Rehm M, Bee N, Endrass B, Wissner M, André E (2007) Too close for comfort?: adapting to the user’s cultural background. In: HCM 2007, pp 85–94Google Scholar
  29. 29.
    Robinson H, MacDonald B, Broadbent E (2014) The role of healthcare robots for older people at home: a review. Int J Socl Robot 6(4):575–591CrossRefGoogle Scholar
  30. 30.
    Ryan JO, Mateas M, Wardrip-Fruin N (2016) A lightweight videogame dialogue manager. In: DiGRA/FDGGoogle Scholar
  31. 31.
    Torta E, Cuijpers RH, Juola JF, van der Pol D (2011) Design of robust robotic proxemic behaviour. In: ICSR 2011, pp 21–30Google Scholar
  32. 32.
    Trovato G, Zecca M, Do M, Terlemez Ö, Kuramochi M, Waibel A, Asfour T, Takanishi A (2015) A novel greeting selection system for a culture-adaptive humanoid robot. Int J Adv Robot Syst 12:34CrossRefGoogle Scholar
  33. 33.
    W3C Owl Working Group and others (2009) OWL 2 web ontology language document overviewGoogle Scholar
  34. 34.
    Wang L, Rau PLP, Evers V, Robinson BK, Hinds P (2010) When in rome: the role of culture & context in adherence to robot recommendations. In: HRI 2010, pp 359–366Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Barbara Bruno
    • 1
    Email author
  • Carmine Tommaso Recchiuto
    • 1
  • Irena Papadopoulos
    • 2
  • Alessandro Saffiotti
    • 3
  • Christina Koulouglioti
    • 2
  • Roberto Menicatti
    • 1
  • Fulvio Mastrogiovanni
    • 1
  • Renato Zaccaria
    • 1
  • Antonio Sgorbissa
    • 1
  1. 1.University of GenoaGenoaItaly
  2. 2.Middlesex University Higher Education CorporationLondonUK
  3. 3.Örebro UniversityÖrebroSweden

Personalised recommendations