Difference between revisions of "Application Ideas for Self-organizing Systems"
From Self-Organization Wiki
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* Apply pattern recognition to identify theft attempts (e.g., persons spying around) | * Apply pattern recognition to identify theft attempts (e.g., persons spying around) | ||
* Self-organization mechanisms are required to adapt to changing patterns and situations | * Self-organization mechanisms are required to adapt to changing patterns and situations | ||
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| + | ==Hybrid Human-Swarm scenarios== | ||
| + | * A human leader ( in a rescue or exploration situation, or in a elderly people home) is followed and assisted by a swarm of SO elements. | ||
| + | * SO elements adjust to environment, can identify the current aim or goal of leader and help achieving it. | ||
| + | * e.g.: build step to elevate leader, help when shopping, overcome obstacles, | ||
Latest revision as of 08:31, 18 July 2011
Contents
Autonomous Transportation Systems
- Pipe systems for transportation of goods
- Autonomous Robots transport goods from A to B
- Goal:
- Agents have to find their way to these pipe systems themselves
- Agents have to move inside the pipe system from A to B
- Problem: if many agents in the system how to organise them to avoid jams, …
Air Traffic Control System
- From top-down system to bottom-up system
- Decentralised
- Constraints, zero-tolerance against error
- Optimise the route
- Agent in the plane knows about other neighbouring planes and applies repulsion
- Identifies the route (on the air)
- Avoid other planes (on the air) – take regulations into account
- Improve quality of service (time departure, ask for priority to leave the airport)
- Reasons
- Many points of failures
- Humans involved – air traffic controller
Self-Organising financial market
- Put software agents into stock markets
- Goal:
- To make market more stable
- Avoid crashes
- To be more in line with economic theory (rational behaviour, faster, act in zero time)
- How (Alternatives)
- All the trading is done by agents
- Trade based on trust
- Build a network of trust, help identify grey-zone
- Population of agents that crawls the market and looks at it in other ways and identifies whether or not product/entity is trustful
- Agents rely on human-like trust (identify healthy from unhealthy products)
- Agents rely in facts on not on emotion or imitation
- Agents identify risks
- Open questions
- How to mix them with human people
- Acceptance?
Mass Panic and Mass Movement
- Use embedded or integrated sensors to sense mass movement
- Use mobile self-organizing nodes (e.g., drones) to get more information in areas of interest
- Reroute persons optimizing time to exit, time to find a free space in a train, etc.
Theft Prevention
- Using cooperating smart homes equipped with cameras, facilities to autonomously switch on light/TV
- Apply pattern recognition to identify theft attempts (e.g., persons spying around)
- Self-organization mechanisms are required to adapt to changing patterns and situations
Hybrid Human-Swarm scenarios
- A human leader ( in a rescue or exploration situation, or in a elderly people home) is followed and assisted by a swarm of SO elements.
- SO elements adjust to environment, can identify the current aim or goal of leader and help achieving it.
- e.g.: build step to elevate leader, help when shopping, overcome obstacles,
