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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Related Experiment Video

Updated: Apr 7, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
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Augmented Robotics Dialog System for Enhancing Human-Robot Interaction.

Fernando Alonso-Martín1, Aĺvaro Castro-González2, Francisco Javier Fernandez de Gorostiza Luengo3

  • 1Robotics Lab, Universidad Carlos III de Madrid, Av. de la Universidad 30, Leganés, Madrid 28911, Spain. fernando.alonso@uc3m.es.

Sensors (Basel, Switzerland)
|July 8, 2015
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Summary
This summary is machine-generated.

This study introduces the Augmented Robotic Dialog System (ARDS) to enhance human-robot interaction (HRI) by enriching conversations with real-time semantic web data. ARDS improves robot pro-activeness and user understanding for a more satisfactory experience.

Keywords:
HRIaugmented dialogaugmented interactioncontextualized dialogdialog systemhuman–robot interactioninteraction systemmultimodal interactionnatural language processingnatural language understandingsocial robots

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Area of Science:

  • Human-Robot Interaction
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Current human-robot interaction (HRI) can be limited in its information exchange capabilities.
  • Augmented reality and related technologies offer enhanced, real-time information delivery.
  • Integrating external knowledge sources can significantly improve interaction quality.

Purpose of the Study:

  • To enhance the quality of human-robot interaction (HRI) through enriched information exchange.
  • To develop a novel dialog manager system that leverages semantic web data.
  • To improve user understanding and satisfaction in HRI scenarios.

Main Methods:

  • Development of the Augmented Robotic Dialog System (ARDS).
  • Implementation of natural language understanding for multimodal input (verbal/written).
  • Utilizing information enrichment techniques to contextualize dialog information with semantic knowledge bases.

Main Results:

  • ARDS provides non-grammar multimodal input and contextualized information.
  • Enriched information enhances robot pro-activeness and topic suggestion.
  • Demonstrated proof of concept for ARDS applications in HRI.

Conclusions:

  • The Augmented Robotic Dialog System (ARDS) effectively enriches HRI by integrating semantic web data.
  • Contextualized information improves interaction coherence and robot pro-activeness.
  • ARDS offers a pathway to more intuitive and satisfactory human-robot communication.