Social robots and artificial agents should be able to interact with the user in the most natural way possible. This work describes the basic principles of a conversation system designed for social robots and artificial agents, which relies on knowledge encoded in the form of an Ontology. Given the knowledge-driven approach, the possibility of expanding the Ontology in run-time, during the verbal interaction with the users is of the utmost importance: this paper also deals with the implementation of a system for the run-time expansion of the knowledge base, thanks to a crowdsourcing approach.

Knowledge-driven conversation for social robots: Exploring crowdsourcing mechanisms for improving the system capabilities

Grassi Lucrezia;Sgorbissa Antonio
2021-01-01

Abstract

Social robots and artificial agents should be able to interact with the user in the most natural way possible. This work describes the basic principles of a conversation system designed for social robots and artificial agents, which relies on knowledge encoded in the form of an Ontology. Given the knowledge-driven approach, the possibility of expanding the Ontology in run-time, during the verbal interaction with the users is of the utmost importance: this paper also deals with the implementation of a system for the run-time expansion of the knowledge base, thanks to a crowdsourcing approach.
File in questo prodotto:
File Dimensione Formato  
short3.pdf

accesso aperto

Descrizione: Contributo in atti di convegno
Tipologia: Documento in Post-print
Dimensione 1.07 MB
Formato Adobe PDF
1.07 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1042200
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact