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Ar#ficial Intelligence
Swarm Intelligence
Prof Alexiei Dingli
Fast changing world • Environment very dynamic and cannot be framed into algorithms
• Social insects and animals are – Efficient – Flexible – Robust
• Solve problems like – Food finding – Self Organisa#on – Path Op#misa#on
Powerful ...
• Swarms – Build colonies – Work in a coordinated manner without having anyone in control
– Build giant structures – Find food sources quickly and efficiently
– Flocks coordinate to move without collisions
– Schools of fish fend off predators
Bees
Bees • Colony coopera.on
• Regulate hive temperature
• Efficiency via Specializa.on: division of
labor in the colony
• Communica.on : Food sources are exploited according to quality and distance from the hive
Wasps
Wasps
• Pulp foragers, water foragers & builders
• Complex nests – Horizontal columns
– Protec.ve covering – Central entrance hole
Termites
Termites
• Cone-‐shaped outer walls and ven.la.on ducts
• Brood chambers in central hive
• Spiral cooling vents • Support pillars
Ants
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 labor between major and minor ants
Problem and Solu#on
• Systems are becoming – Larger and error prone – Complex – Difficult to have a global control
• Swarm intelligence – Robust – Flexible – Rela#vely simple – Self-‐Organised
Defini#on
Any aMempt to design algorithms or distributed problem-‐solving devices inspired by the collec#ve behaviour of social insect colonies and other animal socie#es
Boids
• Created in 1987 • Simulates a flock of birds • Each boid is – Independent – Navigates on its own percep#on of the environment
4 rules of Boids
• Avoidance
• Alignment or Copy
• Centre
• View
Avoidance Rule
• Avoids collisions • Acquire the unfilled space
Alignment Rule
• Copy movements of neighbours by steering towards the average
• Match velocity
Center Rule
• Steer to move towards the average posi#on of flockmates
View Rule
• A boid should move away from any boid that blocks its view
Principles of Flocking • Homogeneity – Every bird has the same behaviour model
• Locality – Mo#on is only influenced by its nearest flock mate
• Collision avoidance • Velocity matching • Flock Cantering – Stay close to nearby flock mates
An In-‐depth Look at Real Ant Behaviour
Interrupt The Flow
The Path Thickens!
The New Shortest Path
Adap.ng to Environment Changes
Adap.ng to Environment Changes
Problems Regarding Swarm Intelligent Systems
• Swarm Intelligent Systems are hard to ‘program’ since the problems are usually difficult to define – Solu.ons are emergent in the systems
– Solu.ons result from behaviors and interac.ons among and between individual agents
Possible Solu.ons to Create Swarm Intelligence Systems • Create a catalog of the collec.ve behaviors
• Model how social insects collec.vely perform tasks – Use this model as a basis upon which ar.ficial varia.ons can be developed
– Model parameters can be tuned within a biologically relevant range or by adding non-‐biological factors to the model
Four Ingredients of Self Organiza.on
• Posi.ve Feedback • Nega.ve Feedback • Amplifica.on of Fluctua.ons -‐ randomness
• Reliance on mul.ple interac.ons
Proper.es of Self-‐Organiza.on
• Crea.on of structures
– Nest, foraging trails, or social organiza.on
• Changes resul.ng from the existence of mul.ple paths of development
– Non-‐coordinated & coordinated phases
• Possible coexistence of mul.ple stable states
– Two equal food sources
Types of Interac.ons For Social Insects
• Direct Interac.ons – Food/liquid exchange, visual contact, chemical contact (pheromones)
• Indirect Interac.ons (S.gmergy) – Individual behavior modifies the environment, which in turn modifies the behavior of other individuals
S.gmergy Example
• Pillar construc.on in termites
S.gmergy in
Ac.on
Ants ≡ Agents
• S.gmergy can be opera.onal – Coordina.on by indirect interac.on is more appealing than direct communica.on
– S.gmergy reduces (or eliminates) communica.ons between agents
From Ants to Algorithms
• Swarm intelligence informa.on allows us to address modeling via: – Problem solving – Algorithms – Real world applica.ons
Modeling
• Observe Phenomenon
• Create a biologically mo.vated model
• Explore model without constraints
A Good Model has...
• Parsimony (simplicity)
• Coherence
• Refutability
• Parameter values correspond to values of their natural counterparts
Travelling Salesperson Problem
Ini#alize Loop /* at this level each loop is called an itera.on */ Each ant is posi#oned on a star#ng node Loop /* at this level each loop is called a step */ Each ant applies a state transi#on rule to incrementally build a solu#on and a local pheromone upda#ng rule Un.l all ants have built a complete solu#on
A global pheromone upda#ng rule is applied Un.l End_condi#on M. Dorigo, L. M. Gambardella : Zp://iridia.ulb.ac.be/pub/mdorigo/journals/IJ.16-‐
TEC97.US.pdf Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman
Problem
Traveling Sales Ants
Robots
• Collec.ve task comple.on • No need for overly complex algorithms
• Adaptable to changing environment
Communica.on Networks
• Rou.ng packets to des.na.on in shortest .me
• Similar to Shortest Route
• Sta.s.cs kept from prior rou.ng (learning from experience)
• Shortest Route
• Conges.on
• Adaptability
• Flexibility
An.fying Website Searching
• Digital-‐Informa.on Pheromones (DIPs)
• Ant World Server
• Transform the web into a gigANTic neural net
Closing Arguments
• S.ll very theore.cal
• No clear boundaries
• Details about inner workings of insect swarms
• The future…???
Dumb parts, properly connected into a swarm, yield smart results.
Kevin Kelly
The Future?
Cleaning Ship Hu
lls
Pest Eradi
ca#on
Job Scheduling
Nanobots?
Ques#ons ?
Exercise
• Implement flocking …
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