Automatic domain modeling for human–robot interaction

Abstract

This paper introduces an approach to automatic domain modeling for human–robot interaction. The proposed approach is symbolic and intended for semantically unconstrained task-oriented human–robot interaction domains. At the specification level, it is cognitively inspired, addressing selected cognitive mechanisms of the human memory system (e.g., integration, semantic categorization, associative learning, etc.) that are relevant for natural language human–robot interaction. We discuss a corpus-based validation of the introduced approach and report on its particular implementation within the conversational agent integrated with a human-like robot.

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Acknowledgements

The presented study was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia (research Grants III44008 and TR32035), and as part of the project “Collaborative strategies of heterogeneous robot activity at solving agriculture missions controlled via intuitive human–robot interfaces” (ID 99), sponsored within the framework of the ERA.Net RUS Plus program. The responsibility for the content of this article lies with the authors.

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Correspondence to Milan Gnjatović.

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Savić, S.Ž., Gnjatović, M., Stefanović, D. et al. Automatic domain modeling for human–robot interaction. Intel Serv Robotics 13, 99–111 (2020). https://doi.org/10.1007/s11370-019-00303-9

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Keywords

  • Human–robot interaction
  • Domain modeling
  • Cognitive mechanisms
  • Focus tree
  • Robot MARKO