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Evolving cognitive-behavioural dependencies in situated agents for behavioural robustness.

Jose A Fernandez-Leon1

  • 1Centre for Computational Neuroscience and Robotics - University of Sussex, Brighton BN1 9QG, United Kingdom. jafphd@gmail.com

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Summary
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Monostable agents, with dynamically limited controllers, exhibit more robust behavior than bistable agents. This enhanced robustness in agents is crucial for tasks like categorical perception, even with environmental changes.

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Area of Science:

  • Artificial intelligence
  • Computational neuroscience
  • Robotics

Background:

  • Investigating agent control mechanisms is key to understanding emergent behavior.
  • Dynamically limited controllers (monostable agents) possess fewer steady states without environmental stimuli compared to bistable agents.
  • Categorical perception, a fundamental cognitive task, requires agents to correlate movements with object types.

Purpose of the Study:

  • To compare the behavioral robustness of monostable and bistable agents.
  • To analyze how controller limitations affect agent performance under perturbations.
  • To explore the role of coupled dynamics in agent robustness.

Main Methods:

  • Evolving agents for categorical perception tasks.
  • Introducing sensorimotor, mutational, and structural perturbations.
  • Analyzing agent behavior and controller states.

Main Results:

  • Monostable agents demonstrated significantly greater behavioral robustness than bistable agents across all tested perturbations.
  • The enhanced robustness of monostable agents was observed in their ability to maintain performance despite environmental and internal changes.
  • Coupled dynamics were identified as a key factor contributing to the superior robustness of monostable agents.

Conclusions:

  • Dynamically limited controllers (monostable agents) confer enhanced behavioral robustness.
  • The findings suggest that reduced steady states and coupled dynamics are beneficial for agent resilience.
  • This research provides insights into designing more robust artificial agents for complex tasks.