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 L.;Recchiuto C.;Sgorbissa A.
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.
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