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Efficiently Evolving Swarm Behaviors Using Grammatical Evolution With PPA-style Behavior Trees
[article]
2022
arXiv
pre-print
Evolving swarm behaviors with artificial agents is computationally expensive and challenging. Because reward structures are often sparse in swarm problems, only a few simulations among hundreds evolve successful swarm behaviors. Additionally, swarm evolutionary algorithms typically rely on ad hoc fitness structures, and novel fitness functions need to be designed for each swarm task. This paper evolves swarm behaviors by systematically combining Postcondition-Precondition-Action (PPA) canonical
arXiv:2203.15776v1
fatcat:vpewwz26jzhjncs53lzdh7s2vq