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Knowledge Representation for Culturally Competent Personal Robots: Requirements, Design Principles, Implementation, and Assessment
University of Genoa, Genoa, Italy.
University of Genoa, Genoa, Italy.
Middlesex University Higher Education Corporation, The Burroughs, Hendon, London, UK.
Örebro universitet, Institutionen för naturvetenskap och teknik.ORCID-id: 0000-0001-8229-1363
Vise andre og tillknytning
2019 (engelsk)Inngår i: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 11, nr 3, s. 515-538Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Springer, 2019. Vol. 11, nr 3, s. 515-538
Emneord [en]
Culture-aware robotics, Companion robot, Knowledge representation
HSV kategori
Identifikatorer
URN: urn:nbn:se:oru:diva-75376DOI: 10.1007/s12369-019-00519-wISI: 000474401100010Scopus ID: 2-s2.0-85068880768OAI: oai:DiVA.org:oru-75376DiVA, id: diva2:1339305
Forskningsfinansiär
EU, Horizon 2020, 737858Tilgjengelig fra: 2019-07-29 Laget: 2019-07-29 Sist oppdatert: 2019-07-29bibliografisk kontrollert

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Saffiotti, AlessandroKoulouglioti, Christina
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