Creating herd behavior by virtual agents using neural networks

The paper focuses on simulating an artificial life in which neural networks (recurrent (RNN) and Long Short Term Memory (LSTM) networks) control prey and predator agents. The research goal was to check whether a simple genetic algorithm evolves the LSTM based controller that competes with the classi...

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Bibliographic Details
Published in:Procedia computer science Vol. 192; pp. 437 - 446
Main Authors: Markowska-Kaczmar, Urszula, Slimak, Adrian
Format: Journal Article
Language:English
Published: Elsevier B.V 2021
Subjects:
2010 MSC: : 92B20
62M45
68T05
adaptive systems
learning
Neural networks
neural networks artificial life and related topics
related approaches
2010 MSC: : 92B20
62M45
68T05
Neural networks
related approaches
learning
neural networks artificial life and related topics
adaptive systems
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary:The paper focuses on simulating an artificial life in which neural networks (recurrent (RNN) and Long Short Term Memory (LSTM) networks) control prey and predator agents. The research goal was to check whether a simple genetic algorithm evolves the LSTM based controller that competes with the classic RNN controller in the real world. We also examined the impact of audio communication within a given species on the survival of agents. Our experiments evidenced the LSTM network results were slightly worse than the RNN controller. We also showed that prey agents developed herd behavior in response to predator pressure. They learned to form herds that allowed them to resist predator attacks. It was also possible to observe the prey agent’s cooperation in searching for food when the plants formed clusters.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2021.08.045