Abstract
In this work, we propose a technique for generation of emergent navigation behavior in autonomous agents which are able to move at the environment using their own vision. In order to achieve this, we apply the Continuous Time Recurrent Artificial Neural Network and the genetic encoding proposed in [1] and [2]. However, we use a new sensorial description, which consists of captured images by a virtual camera, evolving an artificial visual cortex. The experiments show that the agents are able to navigate at the environment and to find the exit, in a non-programmed way and whithout requiring agent’s reprogrammation, using only the visual data passed to the neural network. This technique has the flexibility of being applied in various environments, without displaying a biased forced behavior as a result of a behavioral modeling, as in other techniques.
Highlights
C OMPUTER aided simulation of crowd evacuation allows detailed study, without risk of death, of the behavior of a crowd during the evacuation of an environment in an emergency situation
We applied the same neural network and the same genetic encoding applied to a canonical genetic algorithm (GA) used in [1] and [2], but with a new sensorial description, promoting the evolution of an artificial visual cortex based on images captured by a virtual camera
The agents were able to navigate through the environment and find the exit, in a non-programmed way, using only visual information passed to the neural network, even when a new situation is presented, as in the insertion of pillars in the environment
Summary
C OMPUTER aided simulation of crowd evacuation allows detailed study, without risk of death, of the behavior of a crowd during the evacuation of an environment in an emergency situation For this reason, this type of simulation has been used by researchers interested in reproducing the global behavior of crowds as accurately as possible [3] [4]. It is considered highly relevant to the crowd evacuation simulation, an approach that is able to: generate non-modeled behaviors, compatible with the situation occurring in the virtual environment; provide the possibility of exploration of new situations without the need of any cognitive modeling; and explore new environments whithout requiring agent reprogrammation.
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