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Grounding Context in Embodied Cognitive Robotics.

Diana Valenzo1, Alejandra Ciria2, Guido Schillaci3

  • 1Laboratorio de Robótica Cognitiva, Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, Cuernavaca, Mexico.

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Summary
This summary is machine-generated.

This study clarifies context in cognitive robotics by categorizing it into agent, environment, and task elements. It proposes an interactionist model for autonomous agents to select, plan, and execute tasks flexibly.

Keywords:
behavioral flexibilitycognitive roboticscontextprediction errortask selection

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Area of Science:

  • Cognitive Robotics
  • Artificial Intelligence
  • Behavioral Science

Background:

  • Biological agents exhibit behavioral flexibility, with actions and emotions grounded in context.
  • The concept of context is poorly defined in cognitive robotics research.
  • Existing studies lack a clear framework for understanding and modeling context.

Purpose of the Study:

  • To interpret the notion of context and its core elements from natural agents.
  • To examine how these elements are modeled in cognitive robotics.
  • To propose a new hypothesis on the interactions between contextual elements.

Main Methods:

  • Categorization of global context into agent-related, environmental, and task-related.
  • Analysis of how these elements enable task selection, performance monitoring, and abandonment.
  • Focus on prediction error monitoring for behavioral flexibility.

Main Results:

  • Contextual elements interact to allow agents to select self-relevant tasks and master their environment.
  • Monitoring prediction error is crucial for behavioral flexibility during situated action.
  • Performance monitoring, driven by emotions, guides autonomous behavior.

Conclusions:

  • An interactionist model of context is proposed, integrating agent-related, environmental, and task-related elements.
  • The model is embodied, affective, and situated, guiding artificial agents in autonomous task processing.
  • This framework aims to enhance behavioral flexibility in artificial agents through context processing.