Diversity in swarm robotics with task-independent behavior characterization

Tanja Katharina Kaiser, Heiko Hamann


Evolutionary computation provides methods to automatically generate controllers for swarm robotics. Many approaches rely on optimization and the targeted behavior is quantified in form of a fitness function. Other methods, like novelty search, increase exploration by putting selective pressure on unexplored behavior space using a domain-specific behavioral distance function. In contrast, minimize surprise leads to the emergence of diverse behaviors by using an intrinsic motivation as fitness, that is, high prediction accuracy. We compare a standard genetic algorithm, novelty search and minimize surprise in a swarm robotics setting to evolve diverse behaviors and show that minimize surprise is competitive to novelty search.

Original languageEnglish
Title of host publicationGECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
Number of pages2
PublisherAssociation for Computing Machinery
Publication date08.07.2020
ISBN (Print)978-1-4503-7127-8
Publication statusPublished - 08.07.2020
Event2020 Genetic and Evolutionary Computation Conference - Cancun, Mexico
Duration: 08.07.202012.07.2020
Conference number: 161684


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