Distributed Systems 15. Multiagent systems and swarms Simon Razniewski Faculty of Computer Science...
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![Page 1: Distributed Systems 15. Multiagent systems and swarms Simon Razniewski Faculty of Computer Science Free University of Bozen-Bolzano A.Y. 2014/2015.](https://reader033.fdocuments.us/reader033/viewer/2022051619/56649dff5503460f94ae6fce/html5/thumbnails/1.jpg)
Distributed Systems15. Multiagent systems and swarms
Simon Razniewski
Faculty of Computer ScienceFree University of Bozen-Bolzano
A.Y. 2014/2015
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Reasons for distributed systems so far
• Performance– 1 server can serve 1000 clients– 2 servers can serve ……..?
• Dependability– 1 server has 99% availability– 2 servers have ……….. availability
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Emergence?
• 4000 workers 20 years[http://www.livescience.com/18589-cost-build-great-pyramid-today.html]
1 worker 80000 years?
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Emergence!
• Collaboration of (relatively) simple agent makes larger goals achievable
• Emergence is a process whereby larger entities, patterns, and regularities arise through interactions among smaller or simpler entities that themselves do not exhibit such properties.
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Swarm systems
• Replace central coordination by “intelligent” agents
• Loosely similar to “thin clients” vs “fat clients”• Reason– Increased reliability– Easier handling of complex/nonlinear
environments– Hope for emergence
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An Overview
1. Real world insect examples
2. Theory of swarm intelligence
3. From insects to real algorithms
4. Example applications
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1. Real World Insect Examples
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Bees
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Bees• Colony cooperation
• Regulate hive temperature
• Efficiency via Specialization: division of labour in the colony
• Communication : Food sources are exploited according to quality and distance from the hive
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Wasps
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Wasps
• Pulp foragers, water foragers & builders
• Complex nests– Horizontal columns
– Protective covering
– Central entrance hole
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Ants
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Ants
• Organizing highways to and from their foraging sites by leaving pheromone trails
• Form chains from their own bodies to create a bridge to pull and hold leafs together with silk
• Division of labour between major and minor ants
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Social Insects
• Problem solving benefits include:– Flexible
– Robust
– Decentralized
– Self-Organized
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Summary of Insects
• The complexity and sophistication of Self-Organization is carried out with no clear leader
• What we learn about social insects can be applied to the field of Intelligent System Design
• The modeling of social insects by means of Self-Organization can help design artificial distributed problem solving devices. This is also known as Swarm Intelligent Systems or Multiagent Systems
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2. Swarm Intelligence in Theory
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An In-depth Look at Real Ant Behaviour
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Interrupt The Flow
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The Path Thickens!
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The New Shortest Path
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Adapting to Environment Changes
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Adapting to Environment Changes
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Problems Regarding Swarm Intelligent Systems
• Swarm Intelligent Systems are hard to ‘program’ since the problems are usually difficult to define– Solutions are emergent in the systems
– Solutions result from behaviors and interactions among and between individual agents
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Four Ingredients of Self Organization
• Positive Feedback • Negative Feedback• Amplification of Fluctuations - randomness• Reliance on multiple interactions
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Types of Interactions For Social Insects
• Direct Interactions– Acoustic, visual contact
• Indirect Interactions (Stigmergy)– Individual behavior modifies the environment,
which in turn modifies the behavior of other individuals
– chemical (pheromones)– spatial
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Stigmergy
in
Action
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3. From Insects to Real Algorithms
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Travelling Salesperson ProblemInitialize
Loop /* at this level each loop is called an iteration */Each ant is positioned on a starting node
Loop /* at this level each loop is called a step */Each ant applies a state transition rule to incrementally build a solution and a local pheromone updating rule
Until all ants have built a complete solutionA global pheromone updating rule is appliedUntil End_condition
M. Dorigo, L. M. Gambardella : ftp://iridia.ulb.ac.be/pub/mdorigo/journals/IJ.16-TEC97.US.pdfAnt Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem
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Traveling Sales Ants
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Welcome to the Real World
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Robots
• Collective task completion• No need for overly complex algorithms• Adaptable to changing environment
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Robo soccer
Humanoidshttps://www.youtube.com/watch?v=dhooVgC_0eY
Small sizehttps://www.youtube.com/watch?v=hZE1YQCghLk
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Bird swarms
• Can be simulated with three rules [Reynolds 1986]
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fastcoexist, 2012
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Satellite
Maintenance
HopesMedical
Interacting Chips in
Mundane Objects
Cleaning Ship
Hulls
Pipe Inspection
Pest Eradication
Miniaturiz
ation
Engine Maintenance
Telecommunications
Self-Assembling
Robots
Job Scheduling
Vehicle Routin
g
Data Clustering
Distri
buted M
ail
Syste
ms
Optim
al
Resource
Allocation
Combinatorial
Optimization
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Closing Remarks
• Big expectations, mediocre achievements so far
• No clear boundaries
• The future…???
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Dumb parts, properly connected into a swarm, yield smart results.
Kevin Kelly