A Computational Model of the Evolution of Antipredator Behavior in Situations with Temporal Variation of Danger using Simulated Robots

The threat-sensitive predator avoidance hypothesis states that preys are able to assess the level of danger of the environment by using direct and indirect predator cues. The existence of a neural system which determines this ability has been studied in many animal species like minnows, mosquitoes and wood frogs. What is still under debate is the role of evolution and learning for the emergence of this assessment system. We propose a bio-inspired computing model of how risk management can arise as a result of both factors and prove its impact on fitness in simulated robotic agents equipped with recurrent neural networks and evolved with genetic algorithm. The agents are trained and tested in environments with different level of danger and their performances are analyzed and compared.

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