Dalton Hildreth,
Stephen J. Guy
This work presents a decentralized multi-agent navigation approach
that allows agents to coordinate their motion through local communication.
Our approach allows agents to develop their own emergent language of
communication through an optimization process that simultaneously determines
what agents say in response to their spatial observations and how
agents interpret communication from others to update their motion.
We apply our communication approach together with the TTC-Forces crowd
simulation algorithm (a recent, high performing, anticipatory collision
technique) and show a significant decrease in congestion and bottle-necking
of agents, especially in scenarios where agents benefit from close coordination.
In addition to reaching their goals faster, agents using our approach show
coordinated behaviors including greeting, flocking, following, and grouping.
Furthermore, we observe that communication strategies optimized for one
scenario often continue to provide time-efficient, coordinated motion
between agents when applied to different scenarios. This suggests that the agents
are learning to generalize strategies for coordination through their communication "language".
