Swarm Intelligence

[seminar class]

PRESENTED BY:
Corey Fehr
Merle Good
Shawn Keown
Gordon Fedoriw

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Swarm Intelligence
Ants in the Pants!
An Overview
• Real world insect examples
• Theory of Swarm Intelligence
• From Insects to Realistic
A.I. Algorithms
• Examples of AI applications
• Real World Insect Examples
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
Wasps
• Pulp foragers, water foragers & builders
• Complex nests
– Horizontal columns
– Protective covering
– Central entrance hole
Termites
• Cone-shaped outer walls and ventilation ducts
• Brood chambers in central hive
• Spiral cooling vents
• Support pillars
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
Social Insects
• Problem solving benefits include:
– Flexible
– Robust
– Decentralized
– Self-Organized
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.
Swarm Intelligence in Theory
• An In-depth Look at Real Ant Behaviour
• Interrupt The Flow
• The Path Thickens!
• The New Shortest Path
• Adapting to Environment Changes
• Adapting to Environment Changes
• Ant Pheromone and Food Foraging Demo
• Problems Regarding Swarm Intelligent System
• 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
• Possible Solutions to Create Swarm Intelligence Systems
• Create a catalog of the collective behaviours (Yawn!)
• Model how social insects collectively perform tasks
– Use this model as a basis upon which artificial variations 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 Organization
• Positive Feedback
• Negative Feedback
• Amplification of Fluctuations - randomness
• Reliance on multiple interactions
• Recap: Four Ingredients of
Self Organization
• Positive Feedback
• Negative Feedback
• Amplification of Fluctuations - randomness
• Reliance on multiple interactions
Properties of Self-Organization
• Creation of structures
– Nest, foraging trails, or social organization
• Changes resulting from the existence of multiple paths of development
– Non-coordinated & coordinated phases
• Possible coexistence of multiple stable states
– Two equal food sources
Types of Interactions
For Social Insects
• Direct Interactions
– Food/liquid exchange, visual contact, chemical contact (pheromones)
• Indirect Interactions (Stigmergy)
– Individual behavior modifies the environment, which in turn modifies the behavior of other individuals
• Stigmergy Example
• Pillar construction in termites
Stigmergy in Action
• Ants º Agents
• Stigmergy can be operational
– Coordination by indirect interaction is more appealing than direct communication
– Stigmergy reduces (or eliminates) communications between agents