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Simulated Ant-agent Aspects for Defining an Ant-bots Ontology

Authors:
Colin Chibaya
Ntshuxeko Chibaya
Rifilwe Modiba

Keywords: ant-bot; swarm intelligence; ant-bot ontology

Abstract:
Swarm intelligence systems, wherein robotic devices encoded with collections of discrete abilities executed at individual levels to cause swarm level emergent behaviour are appealing to fields, such as nanotechnology. Such swarm intelligence systems are typically used to solve complex real-life problems at, often, minimal costs. For example, ant colony systems have been proposed for deriving solutions to tough problems by emulating the behaviours of natural ants. Solutions to complex optimisation problems such as the bridge crossing problem, vehicle routing problems, shortest path formation problem, and travelling salesman problem have been presented. This study takes inspiration from the various simulated ant colony systems, investigating the low-level actions and abilities of simulated ant-like robotic devices towards prescribing an ant-bots swarm intelligence ontology. In this context, an ant-bot is assumed to be a tiny naive autonomous robotic device built on the characteristics of simulated ants. On its own, an ant-bot would not achieve anything practical. However, as a swarm, ant-bots can create compelling emergent behaviour. We investigate the discrete aspects of simulated ant agents that cause emergent behaviour and explicitly cogitate them in the design of an ant-bots swarm intelligence ontology. Experimental tests connoted three such aspects as the building blocks of the desired swarm intelligence ontology. First, the swarm space captures metadata about the configuration of the simulated environments, targets, and any global swarm rules. On the other hand, ant-bot context emphasizes the individual abilities and activities of ant-bots. Last, the swarm interaction aspect apprehends the embraced communication mechanism, whether direct or indirect, local, or global, nature inspired, mathematical, biological, or otherwise. Commendably, the swarm intelligence ontology thereof is a mere formal ant-bot knowledge representation model.

Pages: 8 to 13

Copyright: Copyright (c) IARIA, 2023

Publication date: March 13, 2023

Published in: conference

ISSN: 2308-3913

ISBN: 978-1-68558-053-7

Location: Barcelona, Spain

Dates: from March 13, 2023 to March 17, 2023