Ants in the Pants An Overview

1
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

2
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

3
Termites

• Cone-shaped outer walls and ventilation ducts
• Brood chambers in central hive
• Spiral cooling vents
• Support pillars

4
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

5
Social Insects

• Problem solving benefits include
• Flexible
• Robust
• Decentralized
• Self-Organized

6
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.

7
Interrupt The Flow
8
The Path Thickens!
9
The New Shortest Path
10
Adapting to Environment Changes
11
Four Ingredients of Self Organization

• Positive Feedback
• Negative Feedback
• Amplification of Fluctuations - randomness
• Reliance on multiple interactions

12
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

13
(No Transcript)
14
WEB CLUSTERING

• Why?
• The size of the internet has doubling its size
  every year. Estimated 2.1 billion as of July 2001
• Organizing and categorizing document is not
  scalable to the growth of internet.
• Document clustering?
• Is the operation of grouping similar document
  to classes that can be used to obtain an analysis
  of the content.
• Ant clustering algorithm categorize web document
  to different interest domain.

15
Ant Colony Models for Data Clustering

• Data clustering?
• is the task that seek to identify groups of
  similar objects based on the value of their
  attributes.
• Messor sancta ants collect and pile dead corpses
  to form cemeteries (Deneubourg et al. )

f fraction of items in the neighborhood of the
agent k1, k2 threshold constants
16
Ant Colony Models for Data Clustering

• The model later extend by Lume Faieta to
  include distance function d, between data objects
  .
• c is a cell, N(c) is the number of adjacent cells
  of c, alpha is constant

17
Homogeneous Multi-agent System for Document
Clustering

• Main components colony of agents, feature vector
  of web document, 2D grid.
• Rule agent move one step at a time to an
  adjacent cell. Only a single agent and/or a
  single item are allowed to occupy a cell at a
  time. Picking up or dropping item based on Pp
  Pd
• N(c) 8,oi is the item at cell i, g(oi)
  determine the similarity of oi and other item of
  oj, where j E N(c)
• Density

18
Homogeneous Multi-agent System for Document
Clustering
Similarity measure
r is the number of common term in doci and
docj m,n is the total number of term in doci and
docj, respectively. F is the frequency
19
Homogeneous Multi-agent System for Document
Clustering
20
Experimental Results

• Experimental data 84 web pages from 4 different
  categories Business, Computer, Health and
  Science. These web page have 17,776 distinct
  words.
• Use 30x30 toroidal grid
• 15 agents.
• tmax is 300,000. k1 and k2 in 0.01, 0.2
  increment of 0.05 for each run.

21
Experimental Results

• t 0

22
Experimental Results

• t 50,000

23
Experimental Results

• t 200,000

24
Experimental Results

• t 300,000

25
Experimental Result

• Table

26
Particle Swarm Optimization

• Particle Swarm Optimization (PSO) applies to
  concept of social interaction to problem solving.
• It was developed in 1995 by James Kennedy and
  Russ Eberhart Kennedy, J. and Eberhart, R.
  (1995). Particle Swarm Optimization,
  Proceedings of the 1995 IEEE International
  Conference on Neural Networks, pp. 1942-1948,
  IEEE Press. (http//dsp.jpl.nasa.gov/members/paym
  an/swarm/kennedy95-ijcnn.pdf )
• It has been applied successfully to a wide
  variety of search and optimization problems.
• In PSO, a swarm of n individuals communicate
  either directly or indirectly with one another
  search directions (gradients).
• PSO is a simple but powerful search technique.

27
Particle Swarm OptimizationSwarm Topology

• In PSO, there have been two basic topologies used
  in the literature
• Ring Topology (neighborhood of 3)
• Star Topology (global neighborhood)

I0
I0
I1
I1
I4
I4
I2
I3
I2
I3
28
Particle Swarm OptimizationThe Anatomy of a
Particle

• A particle (individual) is composed of
• Three vectors
• The x-vector records the current position
  (location) of the particle in the search space,
• The p-vector records the location of the best
  solution found so far by the particle, and
• The v-vector contains a gradient (direction) for
  which particle will travel in if undisturbed.
• Two fitness values
• The x-fitness records the fitness of the
  x-vector, and
• The p-fitness records the fitness of the
  p-vector.

Ik X ltxk0,xk1,,xkn-1gt P
ltpk0,pk1,,pkn-1gt V ltvk0,vk1,,vkn-1gt x_fitness
? p_fitness ?
29
Particle Swarm OptimizationSwarm Search

• In PSO, particles never die!
• Particles can be seen as simple agents that fly
  through the search space and record (and possibly
  communicate) the best solution that they have
  discovered.
• So the question now is, How does a particle move
  from on location in the search space to another?
• This is done by simply adding the v-vector to the
  x-vector to get another x-vector (Xi Xi Vi).
• Once the particle computes the new Xi it then
  evaluates its new location. If x-fitness is
  better than p-fitness, then Pi Xi and p-fitness
  x-fitness.

30
Particle Swarm Optimization
31
Particle Swarm OptimizationSwarm Search

• Actually, we must adjust the v-vector before
  adding it to the x-vector as follows
• vid vid ?1rnd()(pid-xid)
  ?2rnd()(pgd-xid)
• xid xid vid
• Where i is the particle,
• ?1,?2 are learning rates governing the cognition
  and social components
• Where g represents the index of the particle with
  the best p-fitness, and
• Where d is the dth dimension.

32
Particle Swarm OptimizationSwarm Search

• Intially the values of the velocity vectors are
  randomly generated with the range -Vmax, Vmax
  where Vmax is the maximum value that can be
  assigned to any vid.

33
Particle Swarm OptimizationSwarm Types

• In his paper, Kennedy, J. (1997), The Particle
  Swarm Social Adaptation of Knowledge,
  Proceedings of the 1997 International Conference
  on Evolutionary Computation, pp. 303-308, IEEE
  Press.
• Kennedy identifies 4 types of PSO based on ?1 and
  ?2 .
• Given vid vid ?1rnd()(pid-xid)
  ?2rnd()(pgd-xid)
• xid xid vid
• Full Model (?1, ?2 gt 0)
• Cognition Only (?1 gt 0 and ?2 0),
• Social Only (?1 0 and ?2 gt 0)
• Selfless (?1 0, ?2 gt 0, and g ? i)

34
Particle Swarm OptimizationRelated Issues

• There are a number of related issues concerning
  PSO
• Controlling velocities (determining the best
  value for Vmax),
• Swarm Size,
• Neighborhood Size,
• Updating X and Velocity Vectors,
• Robust Settings for (?1 and ?2),
• An Off-The-Shelf PSO
• Carlisle, A. and Dozier, G. (2001). An
  Off-The-Shelf PSO, Proceedings of the 2001
  Workshop on Particle Swarm Optimization, pp. 1-6,
  Indianapolis, IN. (http//antho.huntingdon.edu/pub
  lications/Off-The-Shelf_PSO.pdf)

35
Particle SwarmControlling Velocities

• When using PSO, it is possible for the magnitude
  of the velocities to become very large.
• Performance can suffer if Vmax is inappropriately
  set.
• Two methods were developed for controlling the
  growth of velocities
• A dynamically adjusted inertia factor, and
• A constriction coefficient.

36
Particle Swarm OptimizationThe Inertia Factor

• When the inertia factor is used, the equation for
  updating velocities is changed to
• vid ?vid ?1rnd()(pid-xid)
  ?2rnd()(pgd-xid)
• Where ? is initialized to 1.0 and is gradually
  reduced over time (measured by cycles through the
  algorithm).

37
Particle Swarm OptimizationSwarm and
Neighborhood Size

• Concerning the swarm size for PSO, as with other
  ECs there is a trade-off between solution quality
  and cost (in terms of function evaluations).
• Global neighborhoods seem to be better in terms
  of computational costs. The performance is
  similar to the ring topology (or neighborhoods
  greater than 3).
• There has been little research on the effects of
  swarm topology on the search behavior of PSO.

38
Particle Swarm OptimizationParticle Update
Methods

• There are two ways that particles can be updated
• Synchronously
• Asynchronously
• Asynchronous update allows for newly discovered
  solutions to be used more quickly

I0
I1
I4
I2
I3
39
The Future?
Telecommunications
Cleaning Ship Hulls
Miniaturization
Medical
Pipe Inspection
Satellite Maintenance
Self-Assembling Robots
Engine Maintenance
Job Scheduling
Combinatorial Optimization
Pest Eradication
Data Clustering
Interacting Chips in Mundane Objects
Vehicle Routing
Distributed Mail Systems
Optimal Resource Allocation
40
Dumb parts, properly connected into a swarm,
yield smart results.