Towards an Aquatic School Inspired Swarm Intelligence Ontology

In broad terms, swarm intelligence refers to the phenomenon where groups of agents exhibit complex behaviour that is notably different from their individual behaviour outside the context of a group. Although commonly observed in nature, these principles have been effectively utilized in computational settings. This study explores swarm intelligence further, discussing the origins of fish inspired computational intelligence, emphasizing notable examples, and core concepts. Key among these concepts are emergent behaviour, distinct behaviours at the group level, and the design of a swarm intelligence ontology that puts together the internal rules guiding swarm behaviour. The focus in this paper is on aquatic swarms, detailing their individual activities, how emergent behaviour arises from these activities, and attempting to simulate such emergent behaviour in computational terms. While much research in swarm intelligence focuses on nature-inspired problem-solving techniques, this study seeks to provide a more comprehensive overview of how aquatic swarm systems fit into this domain. Through a combination of traditional quantitative research and computational methods, this article offers both a theoretical discussion and an interactive visual representation of these principles in the aquatic domain that cause emergent behaviour at swarm level. Throughout, the aim is to propose alternative methods for applying artificial intelligence principles in a manner that goes beyond conventional data-driven approaches.