Toward a context-aware human-robot interaction framework based on cognitive development

Joao Quintas, Goncalo S. Martins, Luis Santos, Paulo Menezes, Jorge Dias

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


The purpose of this paper was to understand how an agent's performance is affected when interaction workflows are incorporated in its information model and decision-making process. Our expectation was that this incorporation could reduce errors and faults on agent's operation, improving its interaction performance. We based this expectation on the existing challenges in designing and implementing artificial social agents, where an approach based on predefined user scenarios and action scripts is insufficient to account for uncertainty in perception or unclear expectations from the user. Therefore, we developed a framework that captures the expected behavior of the agent into descriptive scenarios and then translated these into the agent's information model and used the resulting representation in probabilistic planning and decision making to control interaction. Our results indicated an improvement in terms of specificity while maintaining precision and recall, suggesting that the hypothesis being proposed in our approach is plausible. We believe the presented framework will contribute to the field of cognitive robotics, e.g., by improving the usability of artificial social companions, thus overcoming the limitations imposed by approaches that use predefined static models for an agent's behavior resulting in non-natural interaction.

Original languageBritish English
Article number8360558
Pages (from-to)227-237
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue number1
StatePublished - Jan 2019


  • Active assisted living
  • adaptive systems
  • cloud robotics
  • context awareness
  • decision systems
  • human-machine systems
  • interaction design


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