AbstractSwarm

Complex logistics environments — such as hospitals — demand coordination across many tasks, resources, and shifting conditions, often under uncertainty and time pressure. Traditional approaches rely on centralised planning, but these can be brittle when conditions change rapidly. AbstractSwarm explores an alternative: swarm-based logistics, where large numbers of simple, cooperative AI agents coordinate through decentralised decision-making, local communication, and adaptive behaviour, allowing solutions to emerge collectively as the environment evolves.

By studying how agent swarms handle task allocation, timing, and resource usage in abstract logistics scenarios inspired by real-world domains such as hospital operations, the project advances our understanding of robust, scalable, and flexible intelligence for dynamic environments. The approach is general by design — the methods developed in AbstractSwarm are intended to transfer across logistics domains where uncertainty and complexity are intrinsic.