TY - JOUR
T1 - Collective evolution learning model for visionbased collective motion with collision avoidance
AU - Krongauz, David L.
AU - Lazebnik, Teddy
N1 - Publisher Copyright:
© 2023 Krongauz, Lazebnik.
PY - 2023/5
Y1 - 2023/5
N2 - Collective motion (CM) takes many forms in nature; schools of fish, flocks of birds, and swarms of locusts to name a few. Commonly, during CM the individuals of the group avoid collisions. These CM and collision avoidance (CA) behaviors are based on input from the environment such as smell, air pressure, and vision, all of which are processed by the individual and defined action. In this work, a novel vision-based CM with CA model (i.e., VCMCA) simulating the collective evolution learning process is proposed. In this setting, a learning agent obtains a visual signal about its environment, and throughout trial-and-error over multiple attempts, the individual learns to perform a local CM with CA which emerges into a global CM with CA dynamics. The proposed algorithm was evaluated in the case of locusts' swarms, showing the evolution of these behaviors in a swarm from the learning process of the individual in the swarm. Thus, this work proposes a biologically-inspired learning process to obtain multi-agent multi-objective dynamics.
AB - Collective motion (CM) takes many forms in nature; schools of fish, flocks of birds, and swarms of locusts to name a few. Commonly, during CM the individuals of the group avoid collisions. These CM and collision avoidance (CA) behaviors are based on input from the environment such as smell, air pressure, and vision, all of which are processed by the individual and defined action. In this work, a novel vision-based CM with CA model (i.e., VCMCA) simulating the collective evolution learning process is proposed. In this setting, a learning agent obtains a visual signal about its environment, and throughout trial-and-error over multiple attempts, the individual learns to perform a local CM with CA which emerges into a global CM with CA dynamics. The proposed algorithm was evaluated in the case of locusts' swarms, showing the evolution of these behaviors in a swarm from the learning process of the individual in the swarm. Thus, this work proposes a biologically-inspired learning process to obtain multi-agent multi-objective dynamics.
UR - http://www.scopus.com/inward/record.url?scp=85158851338&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0270318
DO - 10.1371/journal.pone.0270318
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
C2 - 37163523
AN - SCOPUS:85158851338
SN - 1932-6203
VL - 18
JO - PLoS ONE
JF - PLoS ONE
IS - 5 May
M1 - e0270318
ER -